Package 'mlr3extralearners' reference manual

Title:	Extra Learners For mlr3
Description:	Extra learners for use in mlr3.
Authors:	Raphael Sonabend [aut] , Patrick Schratz [aut] , Lorenz A. Kapsner [ctb] , Lennart Schneider [ctb] , Stephen A Lauer [ctb] , Pierre Camilleri [ctb], Javier García [ctb], Keenan Ganz [ctb], Byron Jaeger [ctb], Sebastian Fischer [cre, aut], Zezhi Wang [ctb], John Zobolas [ctb] , Lukas Burk [ctb]
Maintainer:	Sebastian Fischer <[email protected]>
License:	LGPL-3
Version:	1.0.0
Built:	2024-11-07 11:21:30 UTC
Source:	https://github.com/mlr-org/mlr3extralearners

mlr3extralearners: Extra Learners For mlr3

Description

Extra learners for use in mlr3.

Author(s)

Maintainer: Sebastian Fischer [email protected]

Authors:

Raphael Sonabend [email protected] (ORCID)
Patrick Schratz [email protected] (ORCID)

Other contributors:

Lorenz A. Kapsner [email protected] (ORCID) [contributor]
Lennart Schneider [email protected] (ORCID) [contributor]
Stephen A Lauer [email protected] (ORCID) [contributor]
Pierre Camilleri [email protected] [contributor]
Javier García [email protected] [contributor]
Keenan Ganz [email protected] [contributor]
Byron Jaeger [email protected] [contributor]
Zezhi Wang [email protected] [contributor]
John Zobolas [email protected] (ORCID) [contributor]
Lukas Burk [email protected] (ORCID) [contributor]

Create a New Learner

Description

Helper function to create a template for a learner, as well as the test and parameter test. For more details see the mlr3book.

Usage

create_learner(
  path = ".",
  classname,
  type,
  key = tolower(classname),
  algorithm,
  package,
  caller,
  feature_types,
  predict_types,
  properties,
  gh_name = "Unknown",
  label = toproper(algorithm)
)
create_learner(
  path = ".",
  classname,
  type,
  key = tolower(classname),
  algorithm,
  package,
  caller,
  feature_types,
  predict_types,
  properties,
  gh_name = "Unknown",
  label = toproper(algorithm)
)

Arguments

`path`	(`character(1)`) The path to a folder. This is where the files will be created. In case the folder is an R package, the learner file will be create in path/R and the test files will be created in path/tests/testthat. Otherwise all the files will be created in path.
`classname`	(`character(1)`) Suffix for R6 class name passed to LearnerType 'classname'.
`type`	(`character(1)`) See `mlr3::mlr_reflections$task_types$type`.
`key`	(`character(1)`) key for learner, if not provided defaults to the `classname` in all lower case. In combination with `type` it creates the learner's id.
`algorithm`	(`character(1)`) Brief description of the algorithm, like "Linear Model" or "Random Forest". Is used for the title of the help package and as the label (if no other label is provided).
`package`	(`character(1)`) Package from which the learner is implemented.
`caller`	`character(1)` Training function called from the upstream package.
`feature_types`	(`character()`) Feature types that can be handled by the learner, see `mlr3::mlr_reflections$task_feature_types`.
`predict_types`	(`character()`) Prediction types that can be made by the learner, see `mlr3::mlr_reflections$learner_predict_types`.
`properties`	(`character()`) Properties that can be handled by the learner, see `mlr3::mlr_reflections$learner_properties`.
`gh_name`	(`character(1)`) Your GitHub handle, used to add you as the maintainer of the learner. Defaults to `"Unknown"`.
`label`	(`character(1)`) Label for the learner, default is the value of the parameter `algorithm`.

Examples

## Not run: 
path = tempfile()
dir.create(path)
create_learner(
  path = path,
  classname = "Rpart",
  type = "classif",
  key = "rpart",
  algorithm = "Decision Tree",
  package = "rpart",
  caller = "rpart",
  feature_types = c("logical", "integer", "numeric", "factor", "ordered"),
  predict_types = c("response", "prob"),
  properties = c("importance", "missings", "multiclass", "twoclass", "weights"),
  gh_name = "RaphaelS1",
  label = "Regression and Partition Tree"
)

## End(Not run)
## Not run: 
path = tempfile()
dir.create(path)
create_learner(
  path = path,
  classname = "Rpart",
  type = "classif",
  key = "rpart",
  algorithm = "Decision Tree",
  package = "rpart",
  caller = "rpart",
  feature_types = c("logical", "integer", "numeric", "factor", "ordered"),
  predict_types = c("response", "prob"),
  properties = c("importance", "missings", "multiclass", "twoclass", "weights"),
  gh_name = "RaphaelS1",
  label = "Regression and Partition Tree"
)

## End(Not run)

Install Learner Dependencies

Description

Install required dependencies for specified learners. Works for packages on GitHub and CRAN, otherwise must be manually installed.

Usage

install_learners(.keys, repos = "https://cloud.r-project.org", ...)
install_learners(.keys, repos = "https://cloud.r-project.org", ...)

Arguments

`.keys`	(`character()`) Keys passed to mlr_learners specifying learners to install.
`repos`	(`character(1)`) Passed to utils::install.packages.
`...`	(`ANY`) Additional options to pass to utils::install.packages or remotes::install_github.

List Learners in mlr3verse

Description

Lists all learners, properties, and associated packages in a table that can be filtered and queried.

Usage

list_mlr3learners(select = NULL, filter = NULL)
list_mlr3learners(select = NULL, filter = NULL)

Arguments

`select`	`character()` Passed to data.table::subset.
`filter`	`list()` Named list of conditions to filter on, names correspond to column names in table.

Classification Abess Learner

Description

Adaptive best-subset selection for classification. Calls abess::abess() from abess.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.abess")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, abess

Parameters

Id	Type	Default	Levels	Range
family	character	-	binomial, multinomial, ordinal	-
tune.path	character	sequence	sequence, gsection	-
tune.type	character	gic	gic, aic, bic, ebic, cv	-
normalize	integer	NULL		$(-\infty, \infty)$
support.size	untyped	NULL		-
c.max	integer	2		$[1, \infty)$
gs.range	untyped	NULL		-
lambda	numeric	0		$[0, \infty)$
always.include	untyped	NULL		-
group.index	untyped	NULL		-
init.active.set	untyped	NULL		-
splicing.type	integer	2		$[1, 2]$
max.splicing.iter	integer	20		$[1, \infty)$
screening.num	integer	NULL		$[0, \infty)$
important.search	integer	NULL		$[0, \infty)$
warm.start	logical	TRUE	TRUE, FALSE	-
nfolds	integer	5		$(-\infty, \infty)$
foldid	untyped	NULL		-
cov.update	logical	FALSE	TRUE, FALSE	-
newton	character	exact	exact, approx	-
newton.thresh	numeric	1e-06		$[0, \infty)$
max.newton.iter	integer	NULL		$[1, \infty)$
early.stop	logical	FALSE	TRUE, FALSE	-
ic.scale	numeric	1		$[0, \infty)$
num.threads	integer	0		$[0, \infty)$
seed	integer	0		$(-\infty, \infty)$

Initial parameter values

num.threads: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.
family: Depends on the task type, if the parameter family is NULL, it is set to "binomial" for binary classification tasks and to "multinomial" for multiclass classification problems.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifAbess

Methods

Public methods

LearnerClassifAbess$new()
LearnerClassifAbess$selected_features()
LearnerClassifAbess$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifAbess$new()

Method `selected_features()`

Extract the name of selected features from the model by abess::extract().

Usage

LearnerClassifAbess$selected_features()

Returns

The names of selected features

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifAbess$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

abess-team

Examples


# Define the Learner
learner = mlr3::lrn("classif.abess")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.abess")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification AdaBoostM1 Learner

Description

Adaptive boosting algorithm for classification. Calls RWeka::AdaBoostM1() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.AdaBoostM1")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
P	integer	100		$[90, 100]$
Q	logical	FALSE	TRUE, FALSE	-
S	integer	1		$[1, \infty)$
I	integer	10		$[1, \infty)$
W	untyped	"DecisionStump"		-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Parameter changes

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifAdaBoostM1

Methods

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifAdaBoostM1$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifAdaBoostM1$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Freund, Yoav, Schapire, E R, others (1996). “Experiments with a new boosting algorithm.” In icml, volume 96, 148–156. Citeseer.

Examples


# Define the Learner
learner = mlr3::lrn("classif.AdaBoostM1")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.AdaBoostM1")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification BART (Bayesian Additive Regression Trees) Learner

Description

Bayesian Additive Regression Trees are similar to gradient boosting algorithms. The classification problem is solved by 0-1 encoding of the two-class targets and setting the decision threshold to p = 0.5 during the prediction phase. Calls dbarts::bart() from dbarts.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.bart")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, dbarts

Parameters

Id	Type	Default	Levels	Range
ntree	integer	200		$[1, \infty)$
k	numeric	2		$[0, \infty)$
power	numeric	2		$[0, \infty)$
base	numeric	0.95		$[0, 1]$
binaryOffset	numeric	0		$(-\infty, \infty)$
ndpost	integer	1000		$[1, \infty)$
nskip	integer	100		$[0, \infty)$
printevery	integer	100		$[0, \infty)$
keepevery	integer	1		$[1, \infty)$
keeptrainfits	logical	TRUE	TRUE, FALSE	-
usequants	logical	FALSE	TRUE, FALSE	-
numcut	integer	100		$[1, \infty)$
printcutoffs	integer	0		$(-\infty, \infty)$
verbose	logical	FALSE	TRUE, FALSE	-
nthread	integer	1		$(-\infty, \infty)$
keepcall	logical	TRUE	TRUE, FALSE	-
sampleronly	logical	FALSE	TRUE, FALSE	-
seed	integer	NA		$(-\infty, \infty)$
proposalprobs	untyped	NULL		-
splitprobs	untyped	NULL		-
keepsampler	logical	-	TRUE, FALSE	-

Parameter Changes

Parameter: keeptrees
Original: FALSE
New: TRUE
Reason: Required for prediction
Parameter: offset
The parameter is removed, because only dbarts::bart2 allows an offset during training, and therefore the offset parameter in dbarts:::predict.bart is irrelevant for dbarts::dbart.
Parameter: nchain, combineChains, combinechains
The parameters are removed as parallelization of multiple models is handled by future.
Parameter: sigest, sigdf, sigquant, keeptres
Regression only.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifBart

Methods

Public methods

LearnerClassifBart$new()
LearnerClassifBart$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifBart$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifBart$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

ck37

References

Sparapani, Rodney, Spanbauer, Charles, McCulloch, Robert (2021). “Nonparametric machine learning and efficient computation with bayesian additive regression trees: the BART R package.” Journal of Statistical Software, 97, 1–66.

Chipman, A H, George, I E, McCulloch, E R (2010). “BART: Bayesian additive regression trees.” The Annals of Applied Statistics, 4(1), 266–298.

Examples


# Define the Learner
learner = mlr3::lrn("classif.bart")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.bart")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Bayes Network Learner

Description

Bayes Network learning using various search algorithms. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
N removed:
- Initial structure is empty
P removed:
- Maximum number of parents
R removed:
- Random order
mbc removed:
- Applies a Markov Blanket correction to the network structure, after a network structure is learned
S removed:
- Score type
A removed:
- Initial count (alpha)
Reason for change: The parameters are removed because they don't work out of the box and it's unclear how to use them.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.bayes_net")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
D	logical	-	TRUE, FALSE	-
B	untyped	-		-
Q	character	-	global.K2, global.HillClimber, global.SimulatedAnnealing, global.TabuSearch, global.TAN, local.K2, local.HillClimber, local.LAGDHillClimber, local.SimulatedAnnealing, local.TabuSearch, ...	-
E	character	-	estimate.SimpleEstimator, estimate.BMAEstimator, estimate.MultiNomialBMAEstimator	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifBayesNet

Methods

Public methods

LearnerClassifBayesNet$new()
LearnerClassifBayesNet$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifBayesNet$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifBayesNet$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("classif.bayes_net")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.bayes_net")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification C5.0 Learner

Description

Decision Tree Algorithm. Calls C50::C5.0.formula() from C50.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.C50")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, C50

Parameters

Id	Type	Default	Levels	Range
trials	integer	1		$[1, \infty)$
rules	logical	FALSE	TRUE, FALSE	-
costs	untyped	NULL		-
subset	logical	TRUE	TRUE, FALSE	-
bands	integer	-		$[0, 1000]$
winnow	logical	FALSE	TRUE, FALSE	-
noGlobalPruning	logical	FALSE	TRUE, FALSE	-
CF	numeric	0.25		$[0, 1]$
minCases	integer	2		$[0, \infty)$
fuzzyThreshold	logical	FALSE	TRUE, FALSE	-
sample	numeric	0		$[0, 0.999]$
seed	integer	-		$(-\infty, \infty)$
earlyStopping	logical	TRUE	TRUE, FALSE	-
label	untyped	"outcome"		-
na.action	untyped	"stats::na.pass"		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifC50

Methods

Public methods

LearnerClassifC50$new()
LearnerClassifC50$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifC50$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifC50$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Quinlan, Ross J (2014). C4. 5: programs for machine learning. Elsevier.

Gradient Boosted Decision Trees Classification Learner

Description

Gradient boosting algorithm that also supports categorical data. Calls catboost::catboost.train() from package 'catboost'.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.catboost")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, catboost

Parameters

Id	Type	Default	Levels	Range
loss_function_twoclass	character	Logloss	Logloss, CrossEntropy	-
loss_function_multiclass	character	MultiClass	MultiClass, MultiClassOneVsAll	-
learning_rate	numeric	0.03		$[0.001, 1]$
random_seed	integer	0		$[0, \infty)$
l2_leaf_reg	numeric	3		$[0, \infty)$
bootstrap_type	character	-	Bayesian, Bernoulli, MVS, Poisson, No	-
bagging_temperature	numeric	1		$[0, \infty)$
subsample	numeric	-		$[0, 1]$
sampling_frequency	character	PerTreeLevel	PerTree, PerTreeLevel	-
sampling_unit	character	Object	Object, Group	-
mvs_reg	numeric	-		$[0, \infty)$
random_strength	numeric	1		$[0, \infty)$
depth	integer	6		$[1, 16]$
grow_policy	character	SymmetricTree	SymmetricTree, Depthwise, Lossguide	-
min_data_in_leaf	integer	1		$[1, \infty)$
max_leaves	integer	31		$[1, \infty)$
ignored_features	untyped	NULL		-
one_hot_max_size	untyped	FALSE		-
has_time	logical	FALSE	TRUE, FALSE	-
rsm	numeric	1		$[0.001, 1]$
nan_mode	character	Min	Min, Max	-
fold_permutation_block	integer	-		$[1, 256]$
leaf_estimation_method	character	-	Newton, Gradient, Exact	-
leaf_estimation_iterations	integer	-		$[1, \infty)$
leaf_estimation_backtracking	character	AnyImprovement	No, AnyImprovement, Armijo	-
fold_len_multiplier	numeric	2		$[1.001, \infty)$
approx_on_full_history	logical	TRUE	TRUE, FALSE	-
class_weights	untyped	-		-
auto_class_weights	character	None	None, Balanced, SqrtBalanced	-
boosting_type	character	-	Ordered, Plain	-
boost_from_average	logical	-	TRUE, FALSE	-
langevin	logical	FALSE	TRUE, FALSE	-
diffusion_temperature	numeric	10000		$[0, \infty)$
score_function	character	Cosine	Cosine, L2, NewtonCosine, NewtonL2	-
monotone_constraints	untyped	-		-
feature_weights	untyped	-		-
first_feature_use_penalties	untyped	-		-
penalties_coefficient	numeric	1		$[0, \infty)$
per_object_feature_penalties	untyped	-		-
model_shrink_rate	numeric	-		$(-\infty, \infty)$
model_shrink_mode	character	-	Constant, Decreasing	-
target_border	numeric	-		$(-\infty, \infty)$
border_count	integer	-		$[1, 65535]$
feature_border_type	character	GreedyLogSum	Median, Uniform, UniformAndQuantiles, MaxLogSum, MinEntropy, GreedyLogSum	-
per_float_feature_quantization	untyped	-		-
classes_count	integer	-		$[1, \infty)$
thread_count	integer	1		$[-1, \infty)$
task_type	character	CPU	CPU, GPU	-
devices	untyped	-		-
logging_level	character	Silent	Silent, Verbose, Info, Debug	-
metric_period	integer	1		$[1, \infty)$
train_dir	untyped	"catboost_info"		-
model_size_reg	numeric	0.5		$[0, 1]$
allow_writing_files	logical	FALSE	TRUE, FALSE	-
save_snapshot	logical	FALSE	TRUE, FALSE	-
snapshot_file	untyped	-		-
snapshot_interval	integer	600		$[1, \infty)$
simple_ctr	untyped	-		-
combinations_ctr	untyped	-		-
ctr_target_border_count	integer	-		$[1, 255]$
counter_calc_method	character	Full	SkipTest, Full	-
max_ctr_complexity	integer	-		$[1, \infty)$
ctr_leaf_count_limit	integer	-		$[1, \infty)$
store_all_simple_ctr	logical	FALSE	TRUE, FALSE	-
final_ctr_computation_mode	character	Default	Default, Skip	-
verbose	logical	FALSE	TRUE, FALSE	-
ntree_start	integer	0		$[0, \infty)$
ntree_end	integer	0		$[0, \infty)$
early_stopping_rounds	integer	-		$[1, \infty)$
eval_metric	untyped	-		-
use_best_model	logical	-	TRUE, FALSE	-
iterations	integer	1000		$[1, \infty)$

Installation

See https://catboost.ai/en/docs/concepts/r-installation.

Initial parameter values

logging_level:
- Actual default: "Verbose"
- Adjusted default: "Silent"
- Reason for change: consistent with other mlr3 learners
thread_count:
- Actual default: -1
- Adjusted default: 1
- Reason for change: consistent with other mlr3 learners
allow_writing_files:
- Actual default: TRUE
- Adjusted default: FALSE
- Reason for change: consistent with other mlr3 learners
save_snapshot:
- Actual default: TRUE
- Adjusted default: FALSE
- Reason for change: consistent with other mlr3 learners

Early stopping

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifCatboost

Active bindings

internal_valid_scores: The last observation of the validation scores for all metrics. Extracted from model$evaluation_log
internal_tuned_values: Returns the early stopped iterations if early_stopping_rounds was set during training.
validate: How to construct the internal validation data. This parameter can be either NULL, a ratio, "test", or "predefined".

Methods

Public methods

LearnerClassifCatboost$new()
LearnerClassifCatboost$importance()
LearnerClassifCatboost$clone()

Inherited methods

Method `new()`

Create a LearnerClassifCatboost object.

Usage

LearnerClassifCatboost$new()

Method `importance()`

The importance scores are calculated using catboost.get_feature_importance, setting type = "FeatureImportance", returned for 'all'.

Usage

LearnerClassifCatboost$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifCatboost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Dorogush, Veronika A, Ershov, Vasily, Gulin, Andrey (2018). “CatBoost: gradient boosting with categorical features support.” arXiv preprint arXiv:1810.11363.

Examples


# Define the Learner
learner = mlr3::lrn("classif.catboost",
  iterations = 100)

print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.catboost",
  iterations = 100)

print(learner)

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Conditional Random Forest Learner

Description

A random forest based on conditional inference trees (ctree). Calls partykit::cforest() from partykit.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.cforest")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		$[1, \infty)$
replace	logical	FALSE	TRUE, FALSE	-
fraction	numeric	0.632		$[0, 1]$
mtry	integer	-		$[0, \infty)$
mtryratio	numeric	-		$[0, 1]$
applyfun	untyped	-		-
cores	integer	NULL		$(-\infty, \infty)$
trace	logical	FALSE	TRUE, FALSE	-
offset	untyped	-		-
cluster	untyped	-		-
scores	untyped	-		-
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Univariate	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
pargs	untyped	-		-
alpha	numeric	0.05		$[0, 1]$
mincriterion	numeric	0		$[0, 1]$
logmincriterion	numeric	0		$(-\infty, \infty)$
minsplit	integer	20		$[1, \infty)$
minbucket	integer	7		$[1, \infty)$
minprob	numeric	0.01		$[0, 1]$
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
nresample	integer	9999		$[1, \infty)$
tol	numeric	1.490116e-08		$[0, \infty)$
maxsurrogate	integer	0		$[0, \infty)$
numsurrogate	logical	FALSE	TRUE, FALSE	-
maxdepth	integer	Inf		$[0, \infty)$
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		$[0, \infty)$
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
saveinfo	logical	FALSE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
maxvar	integer	-		$[1, \infty)$
OOB	logical	FALSE	TRUE, FALSE	-
simplify	logical	TRUE	TRUE, FALSE	-
scale	logical	TRUE	TRUE, FALSE	-
nperm	integer	1		$[0, \infty)$
risk	character	loglik	loglik, misclassification	-
conditional	logical	FALSE	TRUE, FALSE	-
threshold	numeric	0.2		$(-\infty, \infty)$

Custom mlr3 parameters

mtry:
- This hyperparameter can alternatively be set via the added hyperparameter mtryratio as mtry = max(ceiling(mtryratio * n_features), 1). Note that mtry and mtryratio are mutually exclusive.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifCForest

Methods

Public methods

LearnerClassifCForest$new()
LearnerClassifCForest$oob_error()
LearnerClassifCForest$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifCForest$new()

Method `oob_error()`

The importance scores are calculated using partykit::varimp().

The out-of-bag error, calculated using the OOB predictions from partykit.

Usage

LearnerClassifCForest$oob_error()

Returns

numeric(1).

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifCForest$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Hothorn T, Hornik K, Zeileis A (2006). “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics, 15(3), 651–674. doi:10.1198/106186006x133933, https://doi.org/10.1198/106186006x133933.

Examples


task = tsk("iris")
learner = lrn("classif.cforest", ntree = 50)
splits = partition(task)
learner$train(task, splits$train)
pred = learner$predict(task, splits$test)

task = tsk("iris")
learner = lrn("classif.cforest", ntree = 50)
splits = partition(task)
learner$train(task, splits$train)
pred = learner$predict(task, splits$test)

Classification Conditional Inference Tree Learner

Description

Classification Partition Tree where a significance test is used to determine the univariate splits. Calls partykit::ctree() from partykit.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.ctree")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin

Parameters

Id	Type	Default	Levels	Range
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Bonferroni	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
alpha	numeric	0.05		$[0, 1]$
mincriterion	numeric	0.95		$[0, 1]$
logmincriterion	numeric	-		$(-\infty, \infty)$
minsplit	integer	20		$[1, \infty)$
minbucket	integer	7		$[1, \infty)$
minprob	numeric	0.01		$[0, 1]$
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
nresample	integer	9999		$[1, \infty)$
tol	numeric	-		$[0, \infty)$
maxsurrogate	integer	0		$[0, \infty)$
numsurrogate	logical	FALSE	TRUE, FALSE	-
mtry	integer	Inf		$[0, \infty)$
maxdepth	integer	Inf		$[0, \infty)$
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		$[0, \infty)$
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	FALSE	TRUE, FALSE	-
maxvar	integer	-		$[1, \infty)$
applyfun	untyped	-		-
cores	integer	NULL		$(-\infty, \infty)$
saveinfo	logical	TRUE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
offset	untyped	-		-
cluster	untyped	-		-
scores	untyped	-		-
doFit	logical	TRUE	TRUE, FALSE	-
maxpts	integer	25000		$(-\infty, \infty)$
abseps	numeric	0.001		$[0, \infty)$
releps	numeric	0		$[0, \infty)$

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifCTree

Methods

Public methods

LearnerClassifCTree$new()
LearnerClassifCTree$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifCTree$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifCTree$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Examples


# Define the Learner
learner = mlr3::lrn("classif.ctree")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.ctree")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Decision Stump Learner

Description

Decision Stump Learner. Calls RWeka::DecisionStump() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.decision_stump")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifDecisionStump

Methods

Public methods

LearnerClassifDecisionStump$new()
LearnerClassifDecisionStump$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifDecisionStump$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifDecisionStump$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("classif.decision_stump")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.decision_stump")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Decision Table Learner

Description

Simple Decision Table majority classifier. Calls RWeka::make_Weka_classifier() from RWeka.

Initial parameter values

E:
- Has only 2 out of 4 original evaluation measures : acc and auc with acc being the default
- Reason for change: this learner should only contain evaluation measures appropriate for classification tasks

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
P_best:
- original id: P
D_best:
- original id: D
N_best:
- original id: N
S_best:
- original id: S
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.decision_table")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
S	character	BestFirst	BestFirst, GreedyStepwise	-
X	integer	1		$(-\infty, \infty)$
E	character	acc	acc, auc	-
I	logical	-	TRUE, FALSE	-
R	logical	-	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
P_best	untyped	-		-
D_best	character	1	0, 1, 2	-
N_best	integer	-		$(-\infty, \infty)$
S_best	integer	1		$(-\infty, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifDecisionTable

Methods

Public methods

LearnerClassifDecisionTable$new()
LearnerClassifDecisionTable$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifDecisionTable$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifDecisionTable$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Kohavi R (1995). “The Power of Decision Tables.” In 8th European Conference on Machine Learning, 174–189.

Examples


# Define the Learner
learner = mlr3::lrn("classif.decision_table")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.decision_table")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification MARS (Multivariate Adaptive Regression Splines) Learner

Description

This is an alternative implementation of MARS (Multivariate Adaptive Regression Splines). The classification problem is solved by 0-1 encoding of the two-class targets and setting the decision threshold to p = 0.5 during the prediction phase. MARS is trademarked and thus not used as the name. The name "earth" stands for "Enhanced Adaptive Regression Through Hinges".

Details

Methods for variance estimations are not yet implemented.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.earth")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, earth

Parameters

Id	Type	Default	Levels	Range
wp	untyped	NULL		-
offset	untyped	NULL		-
keepxy	logical	FALSE	TRUE, FALSE	-
trace	character	0	0, .3, .5, 1, 2, 3, 4, 5	-
degree	integer	1		$[1, \infty)$
penalty	numeric	2		$[-1, \infty)$
nk	untyped	NULL		-
thresh	numeric	0.001		$(-\infty, \infty)$
minspan	numeric	0		$[0, \infty)$
endspan	numeric	0		$[0, \infty)$
newvar.penalty	numeric	0		$[0, \infty)$
fast.k	integer	20		$[0, \infty)$
fast.beta	integer	1		$[0, 1]$
linpreds	untyped	FALSE		-
allowed	untyped	-		-
pmethod	character	backward	backward, none, exhaustive, forward, seqrep, cv	-
nprune	integer	-		$[0, \infty)$
nfold	integer	0		$[0, \infty)$
ncross	integer	1		$[0, \infty)$
stratify	logical	TRUE	TRUE, FALSE	-
varmod.method	character	none	none, const, lm, rlm, earth, gam, power, power0, x.lm, x.rlm, ...	-
varmod.exponent	numeric	1		$(-\infty, \infty)$
varmod.conv	numeric	1		$[0, 1]$
varmod.clamp	numeric	0.1		$(-\infty, \infty)$
varmod.minspan	numeric	-3		$(-\infty, \infty)$
Scale.y	logical	FALSE	TRUE, FALSE	-
Adjust.endspan	numeric	2		$(-\infty, \infty)$
Auto.linpreds	logical	TRUE	TRUE, FALSE	-
Force.weights	logical	FALSE	TRUE, FALSE	-
Use.beta.cache	logical	TRUE	TRUE, FALSE	-
Force.xtx.prune	logical	FALSE	TRUE, FALSE	-
Get.leverages	logical	TRUE	TRUE, FALSE	-
Exhaustive.tol	numeric	1e-10		$(-\infty, \infty)$

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifEarth

Methods

Public methods

LearnerClassifEarth$new()
LearnerClassifEarth$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifEarth$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifEarth$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

pkopper

References

Milborrow, Stephen, Hastie, T, Tibshirani, R (2014). “Earth: multivariate adaptive regression spline models.” R package version, 3(7).

Friedman, H J (1991). “Multivariate adaptive regression splines.” The annals of statistics, 19(1), 1–67.

Examples


# Define the Learner
learner = mlr3::lrn("classif.earth")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.earth")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Fast Nearest Neighbour Classification

Description

Fast Nearest Neighbour Classification. Calls FNN::knn() from FNN.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.fnn")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, FNN

Parameters

Id	Type	Default	Levels	Range
k	integer	1		$[1, \infty)$
algorithm	character	kd_tree	kd_tree, cover_tree, brute	-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifFNN

Methods

Public methods

LearnerClassifFNN$new()
LearnerClassifFNN$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifFNN$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifFNN$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Boltz, Sylvain, Debreuve, Eric, Barlaud, Michel (2007). “kNN-based high-dimensional Kullback-Leibler distance for tracking.” In Eighth International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS'07), 16–16. IEEE.

Examples


# Define the Learner
learner = mlr3::lrn("classif.fnn")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.fnn")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Generalized Additive Model Learner

Description

Generalized additive models. Calls mgcv::gam() from package mgcv.

Multiclass classification is not implemented yet.

Formula

A gam formula specific to the task at hand is required for the formula parameter (see example and ?mgcv::formula.gam). Beware, if no formula is provided, a fallback formula is used that will make the gam behave like a glm (this behavior is required for the unit tests). Only features specified in the formula will be used, superseding columns with col_roles "feature" in the task.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.gam")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, mgcv

Parameters

Id	Type	Default	Levels	Range
formula	untyped	-		-
offset	untyped	NULL		-
method	character	GCV.Cp	GCV.Cp, GACV.Cp, REML, P-REML, ML, P-ML	-
optimizer	untyped	c("outer", "newton")		-
scale	numeric	0		$(-\infty, \infty)$
select	logical	FALSE	TRUE, FALSE	-
knots	untyped	NULL		-
sp	untyped	NULL		-
min.sp	untyped	NULL		-
H	untyped	NULL		-
gamma	numeric	1		$[1, \infty)$
paraPen	untyped	NULL		-
G	untyped	NULL		-
in.out	untyped	NULL		-
drop.unused.levels	logical	TRUE	TRUE, FALSE	-
drop.intercept	logical	FALSE	TRUE, FALSE	-
nthreads	integer	1		$[1, \infty)$
irls.reg	numeric	0		$[0, \infty)$
epsilon	numeric	1e-07		$[0, \infty)$
maxit	integer	200		$(-\infty, \infty)$
trace	logical	FALSE	TRUE, FALSE	-
mgcv.tol	numeric	1e-07		$[0, \infty)$
mgcv.half	integer	15		$[0, \infty)$
rank.tol	numeric	1.490116e-08		$[0, \infty)$
nlm	untyped	list()		-
optim	untyped	list()		-
newton	untyped	list()		-
outerPIsteps	integer	0		$[0, \infty)$
idLinksBases	logical	TRUE	TRUE, FALSE	-
scalePenalty	logical	TRUE	TRUE, FALSE	-
efs.lspmax	integer	15		$[0, \infty)$
efs.tol	numeric	0.1		$[0, \infty)$
scale.est	character	fletcher	fletcher, pearson, deviance	-
edge.correct	logical	FALSE	TRUE, FALSE	-
nei	untyped	-		-
ncv.threads	integer	1		$[1, \infty)$
block.size	integer	1000		$(-\infty, \infty)$
unconditional	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGam

Methods

Public methods

LearnerClassifGam$new()
LearnerClassifGam$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifGam$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifGam$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

pierrecamilleri

References

Hastie, J T, Tibshirani, J R (2017). Generalized additive models. Routledge.

Wood, Simon (2012). “mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation.”

Examples


# simple example
t = mlr3::tsk("spam")$filter(1:1000)
l = mlr3::lrn("classif.gam")
l$param_set$set_values(
  formula = type ~ s(george, k = 3) + s(charDollar, k = 3) + s(edu)
)
l$train(t)
l$model

# simple example
t = mlr3::tsk("spam")$filter(1:1000)
l = mlr3::lrn("classif.gam")
l$param_set$set_values(
  formula = type ~ s(george, k = 3) + s(charDollar, k = 3) + s(edu)
)
l$train(t)
l$model

Boosted Generalized Additive Classification Learner

Description

Fit a generalized additive classification model using a boosting algorithm. Calls mboost::gamboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.gamboost")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, mboost

Parameters

Id	Type	Default	Levels	Range
baselearner	character	bbs	bbs, bols, btree	-
dfbase	integer	4		$(-\infty, \infty)$
offset	numeric	NULL		$(-\infty, \infty)$
family	character	Binomial	Binomial, AdaExp, AUC, custom	-
custom.family	untyped	-		-
link	character	logit	logit, probit	-
type	character	adaboost	glm, adaboost	-
mstop	integer	100		$(-\infty, \infty)$
nu	numeric	0.1		$(-\infty, \infty)$
risk	character	inbag	inbag, oobag, none	-
oobweights	untyped	NULL		-
trace	logical	FALSE	TRUE, FALSE	-
stopintern	untyped	FALSE		-
na.action	untyped	stats::na.omit		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGAMBoost

Methods

Public methods

LearnerClassifGAMBoost$new()
LearnerClassifGAMBoost$clone()

Inherited methods

Method `new()`

Create a LearnerClassifGAMBoost object.

Usage

LearnerClassifGAMBoost$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifGAMBoost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

Examples


# Define the Learner
learner = mlr3::lrn("classif.gamboost")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.gamboost")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Gaussian Process Learner

Description

Gaussian process for classification. Calls kernlab::gausspr() from kernlab. Parameters sigma, degree, scale, offset and order are added to make tuning kpar easier. If kpar is provided then these new parameters are ignored. If none are provided then the default "automatic" is used for kpar.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.gausspr")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

Id	Type	Default	Levels	Range
scaled	untyped	TRUE		-
kernel	character	rbfdot	rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot	-
sigma	numeric	-		$(-\infty, \infty)$
degree	numeric	-		$(-\infty, \infty)$
scale	numeric	-		$(-\infty, \infty)$
offset	numeric	-		$(-\infty, \infty)$
order	numeric	-		$(-\infty, \infty)$
kpar	untyped	"automatic"		-
tol	numeric	0.001		$[0, \infty)$
fit	logical	TRUE	TRUE, FALSE	-
na.action	untyped	na.omit		-
coupler	character	minpair	minpair, pkpd	-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGausspr

Methods

Public methods

LearnerClassifGausspr$new()
LearnerClassifGausspr$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifGausspr$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifGausspr$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1–20.

Examples


# Define the Learner
learner = mlr3::lrn("classif.gausspr")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.gausspr")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Gradient Boosting Classification Learner

Description

Gradient Boosting Classification Algorithm. Calls gbm::gbm() from gbm.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.gbm")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, gbm

Parameters

Id	Type	Default	Levels	Range
distribution	character	bernoulli	bernoulli, adaboost, huberized, multinomial	-
n.trees	integer	100		$[1, \infty)$
interaction.depth	integer	1		$[1, \infty)$
n.minobsinnode	integer	10		$[1, \infty)$
shrinkage	numeric	0.001		$[0, \infty)$
bag.fraction	numeric	0.5		$[0, 1]$
train.fraction	numeric	1		$[0, 1]$
cv.folds	integer	0		$(-\infty, \infty)$
keep.data	logical	FALSE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-
n.cores	integer	1		$(-\infty, \infty)$
var.monotone	untyped	-		-

Initial parameter values

keep.data is initialized to FALSE to save memory.
n.cores is initialized to 1 to avoid conflicts with parallelization through future.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGBM

Methods

Public methods

LearnerClassifGBM$new()
LearnerClassifGBM$importance()
LearnerClassifGBM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifGBM$new()

Method `importance()`

The importance scores are extracted by gbm::relative.influence() from the model.

Usage

LearnerClassifGBM$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifGBM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Friedman, H J (2002). “Stochastic gradient boosting.” Computational statistics & data analysis, 38(4), 367–378.

Examples


# Define the Learner
learner = mlr3::lrn("classif.gbm")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.gbm")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Boosted Generalized Linear Classification Learner

Description

Fit a generalized linear classification model using a boosting algorithm. Calls mboost::glmboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.glmboost")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, mboost

Parameters

Id	Type	Default	Levels	Range
offset	numeric	NULL		$(-\infty, \infty)$
family	character	Binomial	Binomial, AdaExp, AUC, custom	-
custom.family	untyped	-		-
link	character	logit	logit, probit	-
type	character	adaboost	glm, adaboost	-
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		$(-\infty, \infty)$
nu	numeric	0.1		$(-\infty, \infty)$
risk	character	inbag	inbag, oobag, none	-
oobweights	untyped	NULL		-
trace	logical	FALSE	TRUE, FALSE	-
stopintern	untyped	FALSE		-
na.action	untyped	stats::na.omit		-
contrasts.arg	untyped	-		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGLMBoost

Methods

Public methods

LearnerClassifGLMBoost$new()
LearnerClassifGLMBoost$clone()

Inherited methods

Method `new()`

Create a LearnerClassifGLMBoost object.

Usage

LearnerClassifGLMBoost$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifGLMBoost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

Examples


# Define the Learner
learner = mlr3::lrn("classif.glmboost")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.glmboost")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Linear Mixed Effect Learner

Description

Generalized linear model with random effects. Calls lme4::glmer() from lme4.

Initial parameter values

family - Is set to stats::binomial(link = "logit").

Formula

Although most mlr3 learners don't allow to specify the formula manually, and automatically set it by valling task$formula(), this learner allows to set the formula because it's core functionality depends it. This means that it might not always use all features that are available in the task. Be aware, that this can sometimes lead to unexpected error messages, because mlr3 checks the compatibility between the learner and the task on all available features.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.glmer")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, lme4

Parameters

Id	Type	Default	Levels	Range
formula	untyped	-		-
start	untyped	NULL		-
verbose	integer	0		$[0, \infty)$
offset	untyped	NULL		-
contrasts	untyped	NULL		-
optimizer	character	-	Nelder_Mead, bobyqa, nlminbwrap, nloptwrap	-
restart_edge	logical	FALSE	TRUE, FALSE	-
boundary.tol	numeric	1e-05		$[0, \infty)$
calc.derivs	logical	TRUE	TRUE, FALSE	-
check.nobs.vs.rankZ	character	ignore	ignore, warning, message, stop	-
check.nobs.vs.nlev	character	stop	ignore, warning, message, stop	-
check.nlev.gtreq.5	character	ignore	ignore, warning, message, stop	-
check.nlev.gtr.1	character	stop	ignore, warning, message, stop	-
check.nobs.vs.nRE	character	stop	ignore, warning, message, stop	-
check.rankX	character	message+drop.cols	message+drop.cols, silent.drop.cols, warn+drop.cols, stop.deficient, ignore	-
check.scaleX	character	warning	warning, stop, silent.rescale, message+rescale, warn+rescale, ignore	-
check.formula.LHS	character	stop	ignore, warning, message, stop	-
family	untyped	"stats::binomial(link = \"logit\")"		-
nAGQ	integer	1		$[0, \infty)$
mustart	untyped	-		-
etastart	untyped	-		-
check.conv.grad	untyped	"lme4::.makeCC(\"warning\", tol = 2e-3, relTol = NULL)"		-
check.conv.singular	untyped	"lme4::.makeCC( action = \"message\", tol = formals(lme4::isSingular)$tol)"		-
check.conv.hess	untyped	"lme4::.makeCC(action = \"warning\", tol = 1e-6)"		-
optCtrl	untyped	list()		-
tolPwrss	untyped	-		-
compDev	logical	TRUE	TRUE, FALSE	-
nAGQ0initStep	logical	TRUE	TRUE, FALSE	-
check.response.not.const	untyped	"stop"		-
newparams	untyped	NULL		-
re.form	untyped	NULL		-
random.only	logical	FALSE	TRUE, FALSE	-
allow.new.levels	logical	FALSE	TRUE, FALSE	-
na.action	untyped	"stats::na.pass"		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifGlmer

Methods

Public methods

LearnerClassifGlmer$new()
LearnerClassifGlmer$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifGlmer$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifGlmer$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sebffischer

References

Bates, M D (2010). “lme4: Mixed-effects modeling with R.”

Examples



# Define the Learner and set parameter values
learner = lrn("classif.glmer",
  formula = credit_risk ~ (1 | credit_history) + job + property + telephone + savings)

# Define a Task
task = tsk("german_credit")
task$select(c("credit_history", "job", "property", "telephone", "savings"))

# Train the learner
learner$train(task)

print(learner$model)

# Define the Learner and set parameter values
learner = lrn("classif.glmer",
  formula = credit_risk ~ (1 | credit_history) + job + property + telephone + savings)

# Define a Task
task = tsk("german_credit")
task$select(c("credit_history", "job", "property", "telephone", "savings"))

# Train the learner
learner$train(task)

print(learner$model)

Classification IBk Learner

Description

Instance based algorithm: K-nearest neighbours regression. Calls RWeka::IBk() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.IBk")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
weight	character	-	I, F	-
K	integer	1		$[1, \infty)$
E	logical	FALSE	TRUE, FALSE	-
W	integer	0		$[0, \infty)$
X	logical	FALSE	TRUE, FALSE	-
A	character	LinearNNSearch	BallTree, CoverTree, FilteredNeighbourSearch, KDTree, LinearNNSearch	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
weight:
- original id: I and F
Reason for change: original I and F params are interdependent (I can only be TRUE when F is FALSE and vice versa). The easiest way to encode this is to combine I and F into one factor param.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifIBk

Methods

Public methods

LearnerClassifIBk$new()
LearnerClassifIBk$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifIBk$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifIBk$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Aha, W D, Kibler, Dennis, Albert, K M (1991). “Instance-based learning algorithms.” Machine learning, 6(1), 37–66.

Examples


# Define the Learner
learner = mlr3::lrn("classif.IBk")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.IBk")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Imbalanced Random Forest Src Learner

Description

Imbalanced Random forest for classification between two classes. Calls randomForestSRC::imbalanced.rfsrc() from from randomForestSRC.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.imbalanced_rfsrc")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, randomForestSRC

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		$[1, \infty)$
method	character	rfq	rfq, brf, standard	-
block.size	integer	10		$[1, \infty)$
fast	logical	FALSE	TRUE, FALSE	-
ratio	numeric	-		$[0, 1]$
mtry	integer	-		$[1, \infty)$
mtry.ratio	numeric	-		$[0, 1]$
nodesize	integer	15		$[1, \infty)$
nodedepth	integer	-		$[1, \infty)$
splitrule	character	gini	gini, auc, entropy	-
nsplit	integer	10		$[0, \infty)$
importance	character	FALSE	FALSE, TRUE, none, permute, random, anti	-
bootstrap	character	by.root	by.root, by.node, none, by.user	-
samptype	character	swor	swor, swr	-
samp	untyped	-		-
membership	logical	FALSE	TRUE, FALSE	-
sampsize	untyped	-		-
sampsize.ratio	numeric	-		$[0, 1]$
na.action	character	na.omit	na.omit, na.impute	-
nimpute	integer	1		$[1, \infty)$
ntime	integer	-		$[1, \infty)$
cause	integer	-		$[1, \infty)$
proximity	character	FALSE	FALSE, TRUE, inbag, oob, all	-
distance	character	FALSE	FALSE, TRUE, inbag, oob, all	-
forest.wt	character	FALSE	FALSE, TRUE, inbag, oob, all	-
xvar.wt	untyped	-		-
split.wt	untyped	-		-
forest	logical	TRUE	TRUE, FALSE	-
var.used	character	FALSE	FALSE, all.trees, by.tree	-
split.depth	character	FALSE	FALSE, all.trees, by.tree	-
seed	integer	-		$(-\infty, -1]$
do.trace	logical	FALSE	TRUE, FALSE	-
statistics	logical	FALSE	TRUE, FALSE	-
get.tree	untyped	-		-
outcome	character	train	train, test	-
ptn.count	integer	0		$[0, \infty)$
cores	integer	1		$[1, \infty)$
save.memory	logical	FALSE	TRUE, FALSE	-
perf.type	character	-	gmean, misclass, brier, none	-
case.depth	logical	FALSE	TRUE, FALSE	-

Custom mlr3 parameters

mtry: This hyperparameter can alternatively be set via the added hyperparameter mtry.ratio as mtry = max(ceiling(mtry.ratio * n_features), 1). Note that mtry and mtry.ratio are mutually exclusive.
sampsize: This hyperparameter can alternatively be set via the added hyperparameter sampsize.ratio as sampsize = max(ceiling(sampsize.ratio * n_obs), 1). Note that sampsize and sampsize.ratio are mutually exclusive.
cores: This value is set as the option rf.cores during training and is set to 1 by default.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifImbalancedRandomForestSRC

Methods

Public methods

LearnerClassifImbalancedRandomForestSRC$new()
LearnerClassifImbalancedRandomForestSRC$importance()
LearnerClassifImbalancedRandomForestSRC$selected_features()
LearnerClassifImbalancedRandomForestSRC$oob_error()
LearnerClassifImbalancedRandomForestSRC$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifImbalancedRandomForestSRC$new()

Method `importance()`

The importance scores are extracted from the slot importance.

Usage

LearnerClassifImbalancedRandomForestSRC$importance()

Returns

Named numeric().

Method `selected_features()`

Selected features are extracted from the model slot var.used.

Usage

LearnerClassifImbalancedRandomForestSRC$selected_features()

Returns

character().

Method `oob_error()`

OOB error extracted from the model slot err.rate.

Usage

LearnerClassifImbalancedRandomForestSRC$oob_error()

Returns

numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifImbalancedRandomForestSRC$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

HarutyunyanLiana

References

O’Brien R, Ishwaran H (2019). “A random forests quantile classifier for class imbalanced data.” Pattern Recognition, 90, 232–249. doi:10.1016/j.patcog.2019.01.036.

Chao C, Leo B (2004). “Using Random Forest to Learn Imbalanced Data.” University of California, Berkeley.

Examples


# Define the Learner
learner = mlr3::lrn("classif.imbalanced_rfsrc", importance = "TRUE")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.imbalanced_rfsrc", importance = "TRUE")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification J48 Learner

Description

Decision tree algorithm. Calls RWeka::IBk() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.J48")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
U	logical	FALSE	TRUE, FALSE	-
O	logical	FALSE	TRUE, FALSE	-
C	numeric	0.25		$[2.22044604925031e-16, 1]$
M	integer	2		$[1, \infty)$
R	logical	FALSE	TRUE, FALSE	-
N	integer	3		$[2, \infty)$
B	logical	FALSE	TRUE, FALSE	-
S	logical	FALSE	TRUE, FALSE	-
L	logical	FALSE	TRUE, FALSE	-
A	logical	FALSE	TRUE, FALSE	-
J	logical	FALSE	TRUE, FALSE	-
Q	integer	1		$[1, \infty)$
doNotMakeSplitPointActualValue	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifJ48

Methods

Public methods

LearnerClassifJ48$new()
LearnerClassifJ48$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifJ48$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifJ48$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Quinlan, Ross J (2014). C4. 5: programs for machine learning. Elsevier.

Examples


# Define the Learner
learner = mlr3::lrn("classif.J48")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.J48")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification JRip Learner

Description

Repeated Incremental Pruning to Produce Error Reduction. Calls RWeka::JRip() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.JRip")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
F	integer	3		$[2, \infty)$
N	numeric	2		$[0, \infty)$
O	integer	2		$[1, \infty)$
D	logical	FALSE	TRUE, FALSE	-
S	integer	1		$[1, \infty)$
E	logical	FALSE	TRUE, FALSE	-
P	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Parameter changes

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifJRip

Methods

Public methods

LearnerClassifJRip$new()
LearnerClassifJRip$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifJRip$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifJRip$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Cohen, W W (1995). “Fast effective rule induction.” In Machine learning proceedings 1995, 115–123. Elsevier.

Examples


# Define the Learner
learner = mlr3::lrn("classif.JRip")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.JRip")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification KStar Learner

Description

Instance-based classifier which differs from other instance-based learners in that it uses an entropy-based distance function. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.kstar")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
B	integer	20		$(-\infty, \infty)$
E	logical	-	TRUE, FALSE	-
M	character	a	a, d, m, n	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifKStar

Methods

Public methods

LearnerClassifKStar$new()
LearnerClassifKStar$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifKStar$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifKStar$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Cleary JG, Trigg LE (1995). “K*: An Instance-based Learner Using an Entropic Distance Measure.” In 12th International Conference on Machine Learning, 108-114.

Examples


# Define the Learner
learner = mlr3::lrn("classif.kstar")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.kstar")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Kernlab Support Vector Machine

Description

Support vector machine for classification. Calls kernlab::ksvm() from kernlab.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.ksvm")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

Id	Type	Default	Levels	Range
scaled	logical	TRUE	TRUE, FALSE	-
type	character	C-svc	C-svc, nu-svc, C-bsvc, spoc-svc, kbb-svc	-
kernel	character	rbfdot	rbfdot, polydot, vanilladot, laplacedot, besseldot, anovadot	-
C	numeric	1		$(-\infty, \infty)$
nu	numeric	0.2		$[0, \infty)$
cache	integer	40		$[1, \infty)$
tol	numeric	0.001		$[0, \infty)$
shrinking	logical	TRUE	TRUE, FALSE	-
sigma	numeric	-		$[0, \infty)$
degree	integer	-		$[1, \infty)$
scale	numeric	-		$[0, \infty)$
order	integer	-		$(-\infty, \infty)$
offset	numeric	-		$(-\infty, \infty)$
coupler	character	minpair	minpair, pkpd	-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifKSVM

Methods

Public methods

LearnerClassifKSVM$new()
LearnerClassifKSVM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifKSVM$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifKSVM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

mboecker

References

Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1–20.

Examples


# Define the Learner
learner = mlr3::lrn("classif.ksvm")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.ksvm")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

LiblineaR Classification Learner

Description

L2 regularized support vector classification. Calls LiblineaR::LiblineaR() from LiblineaR.

Details

Type of SVC depends on type argument:

0 – L2-regularized logistic regression (primal)
1 - L2-regularized L2-loss support vector classification (dual)
3 - L2-regularized L1-loss support vector classification (dual)
2 – L2-regularized L2-loss support vector classification (primal)
4 – Support vector classification by Crammer and Singer
5 - L1-regularized L2-loss support vector classification
6 - L1-regularized logistic regression
7 - L2-regularized logistic regression (dual)

If number of records > number of features, type = 2 is faster than type = 1 (Hsu et al. 2003).

Note that probabilistic predictions are only available for types 0, 6, and 7. The default epsilon value depends on the type parameter, see LiblineaR::LiblineaR.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.liblinear")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “numeric”
Required Packages: mlr3, mlr3extralearners, LiblineaR

Parameters

Id	Type	Default	Levels	Range
type	integer	0		$[0, 7]$
cost	numeric	1		$[0, \infty)$
epsilon	numeric	-		$[0, \infty)$
bias	numeric	1		$(-\infty, \infty)$
cross	integer	0		$[0, \infty)$
verbose	logical	FALSE	TRUE, FALSE	-
wi	untyped	NULL		-
findC	logical	FALSE	TRUE, FALSE	-
useInitC	logical	TRUE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLiblineaR

Methods

Public methods

LearnerClassifLiblineaR$new()
LearnerClassifLiblineaR$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifLiblineaR$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifLiblineaR$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Fan, Rong-En, Chang, Kai-Wei, Hsieh, Cho-Jui, Wang, Xiang-Rui, Lin, Chih-Jen (2008). “LIBLINEAR: A library for large linear classification.” the Journal of machine Learning research, 9, 1871–1874.

Examples


# Define the Learner
learner = mlr3::lrn("classif.liblinear")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.liblinear")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification LightGBM Learner

Description

Gradient boosting algorithm. Calls lightgbm::lightgbm() from lightgbm. The list of parameters can be found here and in the documentation of lightgbm::lgb.train().

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.lightgbm")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, lightgbm

Parameters

Id	Type	Default	Levels	Range
objective	character	-	binary, multiclass, multiclassova	-
eval	untyped	-		-
verbose	integer	1		$(-\infty, \infty)$
record	logical	TRUE	TRUE, FALSE	-
eval_freq	integer	1		$[1, \infty)$
callbacks	untyped	-		-
reset_data	logical	FALSE	TRUE, FALSE	-
boosting	character	gbdt	gbdt, rf, dart, goss	-
linear_tree	logical	FALSE	TRUE, FALSE	-
learning_rate	numeric	0.1		$[0, \infty)$
num_leaves	integer	31		$[1, 131072]$
tree_learner	character	serial	serial, feature, data, voting	-
num_threads	integer	0		$[0, \infty)$
device_type	character	cpu	cpu, gpu	-
seed	integer	-		$(-\infty, \infty)$
deterministic	logical	FALSE	TRUE, FALSE	-
data_sample_strategy	character	bagging	bagging, goss	-
force_col_wise	logical	FALSE	TRUE, FALSE	-
force_row_wise	logical	FALSE	TRUE, FALSE	-
histogram_pool_size	numeric	-1		$(-\infty, \infty)$
max_depth	integer	-1		$(-\infty, \infty)$
min_data_in_leaf	integer	20		$[0, \infty)$
min_sum_hessian_in_leaf	numeric	0.001		$[0, \infty)$
bagging_fraction	numeric	1		$[0, 1]$
pos_bagging_fraction	numeric	1		$[0, 1]$
neg_bagging_fraction	numeric	1		$[0, 1]$
bagging_freq	integer	0		$[0, \infty)$
bagging_seed	integer	3		$(-\infty, \infty)$
bagging_by_query	logical	FALSE	TRUE, FALSE	-
feature_fraction	numeric	1		$[0, 1]$
feature_fraction_bynode	numeric	1		$[0, 1]$
feature_fraction_seed	integer	2		$(-\infty, \infty)$
extra_trees	logical	FALSE	TRUE, FALSE	-
extra_seed	integer	6		$(-\infty, \infty)$
max_delta_step	numeric	0		$(-\infty, \infty)$
lambda_l1	numeric	0		$[0, \infty)$
lambda_l2	numeric	0		$[0, \infty)$
linear_lambda	numeric	0		$[0, \infty)$
min_gain_to_split	numeric	0		$[0, \infty)$
drop_rate	numeric	0.1		$[0, 1]$
max_drop	integer	50		$(-\infty, \infty)$
skip_drop	numeric	0.5		$[0, 1]$
xgboost_dart_mode	logical	FALSE	TRUE, FALSE	-
uniform_drop	logical	FALSE	TRUE, FALSE	-
drop_seed	integer	4		$(-\infty, \infty)$
top_rate	numeric	0.2		$[0, 1]$
other_rate	numeric	0.1		$[0, 1]$
min_data_per_group	integer	100		$[1, \infty)$
max_cat_threshold	integer	32		$[1, \infty)$
cat_l2	numeric	10		$[0, \infty)$
cat_smooth	numeric	10		$[0, \infty)$
max_cat_to_onehot	integer	4		$[1, \infty)$
top_k	integer	20		$[1, \infty)$
monotone_constraints	untyped	NULL		-
monotone_constraints_method	character	basic	basic, intermediate, advanced	-
monotone_penalty	numeric	0		$[0, \infty)$
feature_contri	untyped	NULL		-
forcedsplits_filename	untyped	""		-
refit_decay_rate	numeric	0.9		$[0, 1]$
cegb_tradeoff	numeric	1		$[0, \infty)$
cegb_penalty_split	numeric	0		$[0, \infty)$
cegb_penalty_feature_lazy	untyped	-		-
cegb_penalty_feature_coupled	untyped	-		-
path_smooth	numeric	0		$[0, \infty)$
interaction_constraints	untyped	-		-
use_quantized_grad	logical	TRUE	TRUE, FALSE	-
num_grad_quant_bins	integer	4		$(-\infty, \infty)$
quant_train_renew_leaf	logical	FALSE	TRUE, FALSE	-
stochastic_rounding	logical	TRUE	TRUE, FALSE	-
serializable	logical	TRUE	TRUE, FALSE	-
max_bin	integer	255		$[2, \infty)$
max_bin_by_feature	untyped	NULL		-
min_data_in_bin	integer	3		$[1, \infty)$
bin_construct_sample_cnt	integer	200000		$[1, \infty)$
data_random_seed	integer	1		$(-\infty, \infty)$
is_enable_sparse	logical	TRUE	TRUE, FALSE	-
enable_bundle	logical	TRUE	TRUE, FALSE	-
use_missing	logical	TRUE	TRUE, FALSE	-
zero_as_missing	logical	FALSE	TRUE, FALSE	-
feature_pre_filter	logical	TRUE	TRUE, FALSE	-
pre_partition	logical	FALSE	TRUE, FALSE	-
two_round	logical	FALSE	TRUE, FALSE	-
forcedbins_filename	untyped	""		-
is_unbalance	logical	FALSE	TRUE, FALSE	-
scale_pos_weight	numeric	1		$[0, \infty)$
sigmoid	numeric	1		$[0, \infty)$
boost_from_average	logical	TRUE	TRUE, FALSE	-
eval_at	untyped	1:5		-
multi_error_top_k	integer	1		$[1, \infty)$
auc_mu_weights	untyped	NULL		-
num_machines	integer	1		$[1, \infty)$
local_listen_port	integer	12400		$[1, \infty)$
time_out	integer	120		$[1, \infty)$
machines	untyped	""		-
gpu_platform_id	integer	-1		$(-\infty, \infty)$
gpu_device_id	integer	-1		$(-\infty, \infty)$
gpu_use_dp	logical	FALSE	TRUE, FALSE	-
num_gpu	integer	1		$[1, \infty)$
start_iteration_predict	integer	0		$(-\infty, \infty)$
num_iteration_predict	integer	-1		$(-\infty, \infty)$
pred_early_stop	logical	FALSE	TRUE, FALSE	-
pred_early_stop_freq	integer	10		$(-\infty, \infty)$
pred_early_stop_margin	numeric	10		$(-\infty, \infty)$
num_iterations	integer	100		$[1, \infty)$
early_stopping_rounds	integer	-		$[1, \infty)$
early_stopping_min_delta	numeric	-		$[0, \infty)$
first_metric_only	logical	FALSE	TRUE, FALSE	-

Initial parameter values

num_threads:
- Actual default: 0L
- Initial value: 1L
- Reason for change: Prevents accidental conflicts with future.
verbose:
- Actual default: 1L
- Initial value: -1L
- Reason for change: Prevents accidental conflicts with mlr messaging system.
objective:
- Depends on the task: if binary classification, then this parameter is set to "binary", otherwise "multiclasss" and cannot be changed.

Custom mlr3 parameters

num_class: This parameter is automatically inferred for multiclass tasks and does not have to be set.

Early Stopping and Validation

Early stopping can be used to find the optimal number of boosting rounds. Set early_stopping_rounds to an integer value to monitor the performance of the model on the validation set while training. For information on how to configure the validation set, see the Validation section of mlr3::Learner. The internal validation measure can be set the eval parameter which should be a list of mlr3::Measures, functions, or strings for the internal lightgbm measures. If first_metric_only = FALSE (default), the learner stops when any metric fails to improve.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLightGBM

Active bindings

internal_valid_scores: The last observation of the validation scores for all metrics. Extracted from model$evaluation_log
internal_tuned_values: Returns the early stopped iterations if early_stopping_rounds was set during training.
validate: How to construct the internal validation data. This parameter can be either NULL, a ratio, "test", or "predefined".

Methods

Public methods

LearnerClassifLightGBM$new()
LearnerClassifLightGBM$importance()
LearnerClassifLightGBM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifLightGBM$new()

Method `importance()`

The importance scores are extracted from lbg.importance.

Usage

LearnerClassifLightGBM$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifLightGBM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

kapsner

References

Ke, Guolin, Meng, Qi, Finley, Thomas, Wang, Taifeng, Chen, Wei, Ma, Weidong, Ye, Qiwei, Liu, Tie-Yan (2017). “Lightgbm: A highly efficient gradient boosting decision tree.” Advances in neural information processing systems, 30.

Examples


# Define the Learner
learner = mlr3::lrn("classif.lightgbm")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.lightgbm")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Logistic Model Trees Learner

Description

Classification tree with logistic regression models at the leaves. Calls RWeka::LMT() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.LMT")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
B	logical	FALSE	TRUE, FALSE	-
R	logical	FALSE	TRUE, FALSE	-
C	logical	FALSE	TRUE, FALSE	-
P	logical	FALSE	TRUE, FALSE	-
I	integer	-		$[1, \infty)$
M	integer	15		$[1, \infty)$
W	numeric	0		$[0, 1]$
A	logical	FALSE	TRUE, FALSE	-
doNotMakeSplitPointActualValue	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLMT

Methods

Public methods

LearnerClassifLMT$new()
LearnerClassifLMT$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifLMT$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifLMT$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Landwehr, Niels, Hall, Mark, Frank, Eibe (2005). “Logistic model trees.” Machine learning, 59(1), 161–205.

Examples


# Define the Learner
learner = mlr3::lrn("classif.LMT")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.LMT")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Logistic Regression Learner

Description

Multinomial Logistic Regression model with a ridge estimator. Calls RWeka::Logistic() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.logistic")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
C	logical	FALSE	TRUE, FALSE	-
R	numeric	-		$(-\infty, \infty)$
M	integer	-1		$(-\infty, \infty)$
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLogistic

Methods

Public methods

LearnerClassifLogistic$new()
LearnerClassifLogistic$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifLogistic$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifLogistic$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

le Cessie, S., van Houwelingen, J.C. (1992). “Ridge Estimators in Logistic Regression.” Applied Statistics, 41(1), 191-201.

Examples


# Define the Learner
learner = mlr3::lrn("classif.logistic")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.logistic")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Least Squares Support Vector Machine Learner

Description

Least squares support vector machine for classification. Calls kernlab::lssvm() from kernlab. Parameters sigma, degree, scale, offset, order, length, lambda, and normalized are added to make tuning kpar easier. If kpar is provided then these new parameters are ignored. If none are provided then the default "automatic" is used for kpar.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.lssvm")

Meta Information

Task type: “classif”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

Id	Type	Default	Levels	Range
scaled	untyped	TRUE		-
kernel	character	rbfdot	rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot, stringdot	-
sigma	numeric	-		$(-\infty, \infty)$
degree	numeric	-		$(-\infty, \infty)$
scale	numeric	-		$(-\infty, \infty)$
offset	numeric	-		$(-\infty, \infty)$
order	numeric	-		$(-\infty, \infty)$
length	integer	-		$[0, \infty)$
lambda	numeric	-		$(-\infty, \infty)$
normalized	logical	-	TRUE, FALSE	-
kpar	untyped	"automatic"		-
tau	numeric	0.01		$(-\infty, \infty)$
reduced	logical	TRUE	TRUE, FALSE	-
rank	integer	-		$(-\infty, \infty)$
delta	integer	40		$(-\infty, \infty)$
tol	numeric	1e-04		$(-\infty, \infty)$
fit	logical	TRUE	TRUE, FALSE	-
na.action	untyped	na.omit		-
coupler	character	minpair	minpair, pkpd	-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifLSSVM

Methods

Public methods

LearnerClassifLSSVM$new()
LearnerClassifLSSVM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifLSSVM$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifLSSVM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1–20.

Examples


# Define the Learner
learner = mlr3::lrn("classif.lssvm")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.lssvm")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Model-based Recursive Partitioning Learner

Description

Model-based recursive partitioning algorithm. Calls partykit::mob() from mob.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.mob")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin

Parameters

Id	Type	Default	Levels	Range
rhs	untyped	-		-
fit	untyped	-		-
offset	untyped	-		-
cluster	untyped	-		-
alpha	numeric	0.05		$[0, 1]$
bonferroni	logical	TRUE	TRUE, FALSE	-
minsize	integer	-		$[1, \infty)$
minsplit	integer	-		$[1, \infty)$
minbucket	integer	-		$[1, \infty)$
maxdepth	integer	Inf		$[0, \infty)$
mtry	integer	Inf		$[0, \infty)$
trim	numeric	0.1		$[0, \infty)$
breakties	logical	FALSE	TRUE, FALSE	-
parm	untyped	-		-
dfsplit	integer	-		$[0, \infty)$
prune	untyped	-		-
restart	logical	TRUE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
ytype	character	vector	vector, matrix, data.frame	-
xtype	character	matrix	vector, matrix, data.frame	-
terminal	untyped	"object"		-
inner	untyped	"object"		-
model	logical	TRUE	TRUE, FALSE	-
numsplit	character	left	left, center	-
catsplit	character	binary	binary, multiway	-
vcov	character	opg	opg, info, sandwich	-
ordinal	character	chisq	chisq, max, L2	-
nrep	integer	10000		$[0, \infty)$
applyfun	untyped	-		-
cores	integer	NULL		$(-\infty, \infty)$
additional	untyped	-		-
predict_fun	untyped	-		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifMob

Methods

Public methods

LearnerClassifMob$new()
LearnerClassifMob$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifMob$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifMob$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Examples


library(mlr3)
logit_ = function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  glm(y ~ 1, family = binomial, start = start, ...)
}
learner = LearnerClassifMob$new()
learner$param_set$values$rhs = "."
learner$param_set$values$fit = logit_
learner$param_set$values$additional = list(maxit = 100)
learner$feature_types = c("logical", "integer", "numeric", "factor", "ordered")
learner$properties = c("twoclass", "weights")

predict_fun = function(object, newdata, task, .type) {
  p = unname(predict(object, newdata = newdata, type = "response"))
  levs = task$levels(task$target_names)[[1L]]

  if (.type == "response") {
    ifelse(p < 0.5, levs[1L], levs[2L])
  } else {
    prob_vector_to_matrix(p, levs)
  }
}
task = tsk("breast_cancer")
learner$param_set$values$predict_fun = predict_fun
ids = partition(task)
learner$train(task, row_ids = ids$train)
learner$predict(task, row_ids = ids$test)

library(mlr3)
logit_ = function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  glm(y ~ 1, family = binomial, start = start, ...)
}
learner = LearnerClassifMob$new()
learner$param_set$values$rhs = "."
learner$param_set$values$fit = logit_
learner$param_set$values$additional = list(maxit = 100)
learner$feature_types = c("logical", "integer", "numeric", "factor", "ordered")
learner$properties = c("twoclass", "weights")

predict_fun = function(object, newdata, task, .type) {
  p = unname(predict(object, newdata = newdata, type = "response"))
  levs = task$levels(task$target_names)[[1L]]

  if (.type == "response") {
    ifelse(p < 0.5, levs[1L], levs[2L])
  } else {
    prob_vector_to_matrix(p, levs)
  }
}
task = tsk("breast_cancer")
learner$param_set$values$predict_fun = predict_fun
ids = partition(task)
learner$train(task, row_ids = ids$train)
learner$predict(task, row_ids = ids$test)

Classification MultilayerPerceptron Learner

Description

Classifier that uses backpropagation to learn a multi-layer perceptron. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
G removed:
- GUI will be opened
Reason for change: The parameter is removed because we don't want to launch GUI.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.multilayer_perceptron")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
L	numeric	0.3		$[0, 1]$
M	numeric	0.2		$[0, 1]$
N	integer	500		$[1, \infty)$
V	numeric	0		$[0, 100]$
S	integer	0		$[0, \infty)$
E	integer	20		$[1, \infty)$
A	logical	FALSE	TRUE, FALSE	-
B	logical	FALSE	TRUE, FALSE	-
H	untyped	"a"		-
C	logical	FALSE	TRUE, FALSE	-
I	logical	FALSE	TRUE, FALSE	-
R	logical	FALSE	TRUE, FALSE	-
D	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifMultilayerPerceptron

Methods

Public methods

LearnerClassifMultilayerPerceptron$new()
LearnerClassifMultilayerPerceptron$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifMultilayerPerceptron$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifMultilayerPerceptron$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("classif.multilayer_perceptron")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.multilayer_perceptron")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Multinomial Naive Bayes Learner From Weka

Description

Multinomial Naive Bayes classifier. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.naive_bayes_multinomial")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifNaiveBayesMultinomial

Methods

Public methods

LearnerClassifNaiveBayesMultinomial$new()
LearnerClassifNaiveBayesMultinomial$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifNaiveBayesMultinomial$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifNaiveBayesMultinomial$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Mccallum A, Nigam K (1998). “A Comparison of Event Models for Naive Bayes Text Classification.” In AAAI-98 Workshop on 'Learning for Text Categorization'.

Examples


# Define the Learner
learner = mlr3::lrn("classif.naive_bayes_multinomial")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.naive_bayes_multinomial")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Naive Bayes Learner From Weka

Description

Naive Bayes Classifier Using Estimator Classes. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.naive_bayes_weka")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
K	logical	FALSE	TRUE, FALSE	-
D	logical	FALSE	TRUE, FALSE	-
O	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifNaiveBayesWeka

Methods

Public methods

LearnerClassifNaiveBayesWeka$new()
LearnerClassifNaiveBayesWeka$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifNaiveBayesWeka$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifNaiveBayesWeka$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

John GH, Langley P (1995). “Estimating Continuous Distributions in Bayesian Classifiers.” In Eleventh Conference on Uncertainty in Artificial Intelligence, 338-345.

Examples


# Define the Learner
learner = mlr3::lrn("classif.naive_bayes_weka")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.naive_bayes_weka")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification OneR Learner

Description

One Rule classification algorithm that yields an extremely simple model. Calls RWeka::OneR() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.OneR")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
B	integer	6		$[1, \infty)$
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifOneR

Methods

Public methods

LearnerClassifOneR$new()
LearnerClassifOneR$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifOneR$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifOneR$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Holte, C R (1993). “Very simple classification rules perform well on most commonly used datasets.” Machine learning, 11(1), 63–90.

Examples


# Define the Learner
learner = mlr3::lrn("classif.OneR")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.OneR")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification PART Learner

Description

Regression partition tree. Calls RWeka::PART() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.PART")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
C	numeric	0.25		$[2.22044604925031e-16, 1]$
M	integer	2		$[1, \infty)$
R	logical	FALSE	TRUE, FALSE	-
N	integer	3		$[1, \infty)$
B	logical	FALSE	TRUE, FALSE	-
U	logical	FALSE	TRUE, FALSE	-
J	logical	FALSE	TRUE, FALSE	-
Q	integer	1		$[1, \infty)$
doNotMakeSplitPointActualValue	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifPART

Methods

Public methods

LearnerClassifPART$new()
LearnerClassifPART$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifPART$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifPART$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Frank, Eibe, Witten, H I (1998). “Generating accurate rule sets without global optimization.”

Examples


# Define the Learner
learner = mlr3::lrn("classif.PART")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.PART")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Priority Lasso Learner

Description

Patient outcome prediction based on multi-omics data taking practitioners’ preferences into account. Calls prioritylasso::prioritylasso() from prioritylasso.

Initial parameter values

family is set to "binomial" and canno be changed

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.priority_lasso")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, prioritylasso

Parameters

Id	Type	Default	Levels	Range
blocks	untyped	-		-
type.measure	character	-	class, auc	-
max.coef	untyped	NULL		-
block1.penalization	logical	TRUE	TRUE, FALSE	-
lambda.type	character	lambda.min	lambda.min, lambda.1se	-
standardize	logical	TRUE	TRUE, FALSE	-
nfolds	integer	5		$[1, \infty)$
foldid	untyped	NULL		-
cvoffset	logical	FALSE	TRUE, FALSE	-
cvoffsetnfolds	integer	10		$[1, \infty)$
return.x	logical	TRUE	TRUE, FALSE	-
handle.missingtestdata	character	-	none, omit.prediction, set.zero, impute.block	-
include.allintercepts	logical	FALSE	TRUE, FALSE	-
use.blocks	untyped	"all"		-
alignment	character	lambda	lambda, fraction	-
alpha	numeric	1		$[0, 1]$
big	numeric	9.9e+35		$(-\infty, \infty)$
devmax	numeric	0.999		$[0, 1]$
dfmax	integer	-		$[0, \infty)$
eps	numeric	1e-06		$[0, 1]$
epsnr	numeric	1e-08		$[0, 1]$
exclude	untyped	-		-
exmx	numeric	250		$(-\infty, \infty)$
fdev	numeric	1e-05		$[0, 1]$
gamma	untyped	-		-
grouped	logical	TRUE	TRUE, FALSE	-
intercept	logical	TRUE	TRUE, FALSE	-
keep	logical	FALSE	TRUE, FALSE	-
lambda	untyped	-		-
lambda.min.ratio	numeric	-		$[0, 1]$
lower.limits	untyped	-Inf		-
maxit	integer	100000		$[1, \infty)$
mnlam	integer	5		$[1, \infty)$
mxit	integer	100		$[1, \infty)$
mxitnr	integer	25		$[1, \infty)$
nlambda	integer	100		$[1, \infty)$
offset	untyped	NULL		-
parallel	logical	FALSE	TRUE, FALSE	-
penalty.factor	untyped	-		-
pmax	integer	-		$[0, \infty)$
pmin	numeric	1e-09		$[0, 1]$
prec	numeric	1e-10		$(-\infty, \infty)$
predict.gamma	numeric	gamma.1se		$(-\infty, \infty)$
relax	logical	FALSE	TRUE, FALSE	-
s	numeric	lambda.1se		$[0, 1]$
standardize.response	logical	FALSE	TRUE, FALSE	-
thresh	numeric	1e-07		$[0, \infty)$
trace.it	integer	0		$[0, 1]$
type.gaussian	character	-	covariance, naive	-
type.logistic	character	Newton	Newton, modified.Newton	-
type.multinomial	character	ungrouped	ungrouped, grouped	-
upper.limits	untyped	Inf		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifPriorityLasso

Methods

Public methods

LearnerClassifPriorityLasso$new()
LearnerClassifPriorityLasso$selected_features()
LearnerClassifPriorityLasso$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifPriorityLasso$new()

Method `selected_features()`

Selected features, i.e. those where the coefficient is non-zero.

Usage

LearnerClassifPriorityLasso$selected_features()

Returns

character().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifPriorityLasso$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

HarutyunyanLiana

References

Simon K, Vindi J, Roman H, Tobias H, Anne-Laure B (2018). “Priority-Lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data.” BMC Bioinformatics, 19. doi:10.1186/s12859-018-2344-6.

Examples


# Define the Learner and set parameter values
learner = lrn("classif.priority_lasso", type.measure = "auc",
  blocks = list(bp1 = 1:4, bp2 = 5:9, bp3 = 10:28, bp4 = 29:1028))
print(learner)

# Define a Task
task = mlr3::as_task_classif(prioritylasso::pl_data, target = "pl_out")

# Train the learner
learner$train(task)

# print the model
print(learner$model)

# Define the Learner and set parameter values
learner = lrn("classif.priority_lasso", type.measure = "auc",
  blocks = list(bp1 = 1:4, bp2 = 5:9, bp3 = 10:28, bp4 = 29:1028))
print(learner)

# Define a Task
task = mlr3::as_task_classif(prioritylasso::pl_data, target = "pl_out")

# Train the learner
learner$train(task)

# print the model
print(learner$model)

Classification Random Forest Learner from Weka

Description

Class for constructing a random forest. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
store_out_of_bag_predictions:
- original id: store-out-of-bag-predictions
output_out_of_bag_complexity_statistics:
- original id: output-out-of-bag-complexity-statistics
num_slots:
- original id: num-slots
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
attribute-importance removed:
- Compute and output attribute importance (mean impurity decrease method)
Reason for change: The parameter is removed because it's unclear how to actually use it.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.random_forest_weka")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
P	numeric	100		$[0, 100]$
O	logical	FALSE	TRUE, FALSE	-
store_out_of_bag_predictions	logical	FALSE	TRUE, FALSE	-
output_out_of_bag_complexity_statistics	logical	FALSE	TRUE, FALSE	-
print	logical	FALSE	TRUE, FALSE	-
I	integer	100		$[1, \infty)$
num_slots	integer	1		$(-\infty, \infty)$
K	integer	0		$(-\infty, \infty)$
M	integer	1		$[1, \infty)$
V	numeric	0.001		$(-\infty, \infty)$
S	integer	1		$(-\infty, \infty)$
depth	integer	0		$[0, \infty)$
N	integer	0		$(-\infty, \infty)$
U	logical	FALSE	TRUE, FALSE	-
B	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomForestWeka

Methods

Public methods

LearnerClassifRandomForestWeka$new()
LearnerClassifRandomForestWeka$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForestWeka$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomForestWeka$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

Examples


# Define the Learner
learner = mlr3::lrn("classif.random_forest_weka")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.random_forest_weka")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Random Tree Learner

Description

Tree that considers K randomly chosen attributes at each node. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.random_tree")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
K	integer	0		$[0, \infty)$
M	integer	1		$[1, \infty)$
V	numeric	0.001		$(-\infty, \infty)$
S	integer	1		$(-\infty, \infty)$
depth	integer	0		$[0, \infty)$
N	integer	0		$[0, \infty)$
U	logical	FALSE	TRUE, FALSE	-
B	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomTree

Methods

Public methods

LearnerClassifRandomTree$new()
LearnerClassifRandomTree$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomTree$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomTree$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("classif.random_tree")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.random_tree")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Random Forest Learner

Description

Random forest for classification. Calls randomForest::randomForest() from randomForest.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.randomForest")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, randomForest

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		$[1, \infty)$
mtry	integer	-		$[1, \infty)$
replace	logical	TRUE	TRUE, FALSE	-
classwt	untyped	NULL		-
cutoff	untyped	-		-
strata	untyped	-		-
sampsize	untyped	-		-
nodesize	integer	1		$[1, \infty)$
maxnodes	integer	-		$[1, \infty)$
importance	character	FALSE	accuracy, gini, none	-
localImp	logical	FALSE	TRUE, FALSE	-
proximity	logical	FALSE	TRUE, FALSE	-
oob.prox	logical	-	TRUE, FALSE	-
norm.votes	logical	TRUE	TRUE, FALSE	-
do.trace	logical	FALSE	TRUE, FALSE	-
keep.forest	logical	TRUE	TRUE, FALSE	-
keep.inbag	logical	FALSE	TRUE, FALSE	-
predict.all	logical	FALSE	TRUE, FALSE	-
nodes	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomForest

Methods

Public methods

LearnerClassifRandomForest$new()
LearnerClassifRandomForest$importance()
LearnerClassifRandomForest$oob_error()
LearnerClassifRandomForest$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForest$new()

Method `importance()`

The importance scores are extracted from the slot importance. Parameter 'importance' must be set to either "accuracy" or "gini".

Usage

LearnerClassifRandomForest$importance()

Returns

Named numeric().

Method `oob_error()`

OOB errors are extracted from the model slot err.rate.

Usage

LearnerClassifRandomForest$oob_error()

Returns

numeric(1).

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomForest$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

pat-s

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

Examples


# Define the Learner
learner = mlr3::lrn("classif.randomForest", importance = "accuracy")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.randomForest", importance = "accuracy")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Decision Tree Learner

Description

Fast decision tree learner. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.reptree")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
M	integer	2		$(-\infty, \infty)$
V	numeric	0.001		$(-\infty, \infty)$
N	integer	3		$(-\infty, \infty)$
S	integer	1		$(-\infty, \infty)$
P	logical	-	TRUE, FALSE	-
L	integer	-1		$(-\infty, \infty)$
I	integer	0		$(-\infty, \infty)$
R	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifREPTree

Methods

Public methods

LearnerClassifREPTree$new()
LearnerClassifREPTree$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifREPTree$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifREPTree$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("classif.reptree")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.reptree")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Random Forest SRC Learner

Description

Random forest for classification. Calls randomForestSRC::rfsrc() from randomForestSRC.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.rfsrc")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, randomForestSRC

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		$[1, \infty)$
mtry	integer	-		$[1, \infty)$
mtry.ratio	numeric	-		$[0, 1]$
nodesize	integer	15		$[1, \infty)$
nodedepth	integer	-		$[1, \infty)$
splitrule	character	gini	gini, auc, entropy	-
nsplit	integer	10		$[0, \infty)$
importance	character	FALSE	FALSE, TRUE, none, permute, random, anti	-
block.size	integer	10		$[1, \infty)$
bootstrap	character	by.root	by.root, by.node, none, by.user	-
samptype	character	swor	swor, swr	-
samp	untyped	-		-
membership	logical	FALSE	TRUE, FALSE	-
sampsize	untyped	-		-
sampsize.ratio	numeric	-		$[0, 1]$
na.action	character	na.omit	na.omit, na.impute	-
nimpute	integer	1		$[1, \infty)$
ntime	integer	-		$[1, \infty)$
cause	integer	-		$[1, \infty)$
proximity	character	FALSE	FALSE, TRUE, inbag, oob, all	-
distance	character	FALSE	FALSE, TRUE, inbag, oob, all	-
forest.wt	character	FALSE	FALSE, TRUE, inbag, oob, all	-
xvar.wt	untyped	-		-
split.wt	untyped	-		-
forest	logical	TRUE	TRUE, FALSE	-
var.used	character	FALSE	FALSE, all.trees, by.tree	-
split.depth	character	FALSE	FALSE, all.trees, by.tree	-
seed	integer	-		$(-\infty, -1]$
do.trace	logical	FALSE	TRUE, FALSE	-
statistics	logical	FALSE	TRUE, FALSE	-
get.tree	untyped	-		-
outcome	character	train	train, test	-
ptn.count	integer	0		$[0, \infty)$
cores	integer	1		$[1, \infty)$
save.memory	logical	FALSE	TRUE, FALSE	-
perf.type	character	-	gmean, misclass, brier, none	-
case.depth	logical	FALSE	TRUE, FALSE	-

Custom mlr3 parameters

mtry: This hyperparameter can alternatively be set via the added hyperparameter mtry.ratio as mtry = max(ceiling(mtry.ratio * n_features), 1). Note that mtry and mtry.ratio are mutually exclusive.
sampsize: This hyperparameter can alternatively be set via the added hyperparameter sampsize.ratio as sampsize = max(ceiling(sampsize.ratio * n_obs), 1). Note that sampsize and sampsize.ratio are mutually exclusive.
cores: This value is set as the option rf.cores during training and is set to 1 by default.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomForestSRC

Methods

Public methods

LearnerClassifRandomForestSRC$new()
LearnerClassifRandomForestSRC$importance()
LearnerClassifRandomForestSRC$selected_features()
LearnerClassifRandomForestSRC$oob_error()
LearnerClassifRandomForestSRC$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomForestSRC$new()

Method `importance()`

The importance scores are extracted from the model slot importance, returned for 'all'.

Usage

LearnerClassifRandomForestSRC$importance()

Returns

Named numeric().

Method `selected_features()`

Selected features are extracted from the model slot var.used.

Usage

LearnerClassifRandomForestSRC$selected_features()

Returns

character().

Method `oob_error()`

OOB error extracted from the model slot err.rate.

Usage

LearnerClassifRandomForestSRC$oob_error()

Returns

numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomForestSRC$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

Examples


# Define the Learner
learner = mlr3::lrn("classif.rfsrc", importance = "TRUE")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.rfsrc", importance = "TRUE")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Random Planted Forest Learner

Description

Random Planted Forest: A directly interpretable tree ensemble.

Calls randomPlantedForest::rpf() from 'randomPlantedForest'.

Initial parameter values

loss:
- Actual default: "L2".
- Initial value: "exponential".
- Reason for change: Using "L2" (or "L1") loss does not guarantee predictions are valid probabilities and more akin to the linear predictor of a GLM.

Custom mlr3 parameters

max_interaction:
- This hyperparameter can alternatively be set via max_interaction_ratio as max_interaction = max(ceiling(max_interaction_ratio * n_features), 1). The parameter max_interaction_limit can optionally be set as an upper bound, such that max_interaction_ratio * min(n_features, max_interaction_limit) is used instead. This is analogous to mtry.ratio in classif.ranger, with max_interaction_limit as an additional constraint. The parameter max_interaction_limit is initialized to Inf.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.rpf")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, randomPlantedForest

Parameters

Id	Type	Default	Levels	Range
max_interaction	integer	1		$[0, \infty)$
max_interaction_ratio	numeric	-		$[0, 1]$
max_interaction_limit	integer	-		$[1, \infty)$
ntrees	integer	50		$[1, \infty)$
splits	integer	30		$[1, \infty)$
split_try	integer	10		$[1, \infty)$
t_try	numeric	0.4		$[0, 1]$
loss	character	L2	L1, L2, logit, exponential	-
delta	numeric	1		$[0, 1]$
epsilon	numeric	0.1		$[0, 1]$
deterministic	logical	FALSE	TRUE, FALSE	-
nthreads	integer	1		$[1, \infty)$
cv	logical	FALSE	TRUE, FALSE	-
purify	logical	FALSE	TRUE, FALSE	-

Installation

Package 'randomPlantedForest' is not on CRAN and has to be installed from GitHub via remotes::install_github("PlantedML/randomPlantedForest").

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifRandomPlantedForest

Methods

Public methods

LearnerClassifRandomPlantedForest$new()
LearnerClassifRandomPlantedForest$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifRandomPlantedForest$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifRandomPlantedForest$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

jemus42

References

Hiabu, Munir, Mammen, Enno, Meyer, T. J (2023). “Random Planted Forest: a directly interpretable tree ensemble.” arXiv preprint arXiv:2012.14563. doi:10.48550/ARXIV.2012.14563.

Examples


# Define the Learner
learner = mlr3::lrn("classif.rpf")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.rpf")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Stochastic Gradient Descent Learner

Description

Stochastic Gradient Descent for learning various linear models. Calls RWeka::make_Weka_classifier() from RWeka.

Initial parameter values

F:
- Has only 2 out of 5 original loss functions: 0 = hinge loss (SVM) and 1 = log loss (logistic regression) with 0 (hinge loss) still being the default
- Reason for change: this learner should only contain loss functions appropriate for classification tasks

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.sgd")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
F	character	0	0, 1	-
L	numeric	0.01		$(-\infty, \infty)$
R	numeric	1e-04		$(-\infty, \infty)$
E	integer	500		$(-\infty, \infty)$
C	numeric	0.001		$(-\infty, \infty)$
N	logical	-	TRUE, FALSE	-
M	logical	-	TRUE, FALSE	-
S	integer	1		$(-\infty, \infty)$
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSGD

Methods

Public methods

LearnerClassifSGD$new()
LearnerClassifSGD$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifSGD$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifSGD$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("classif.sgd")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.sgd")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification SimpleLogistic Learner

Description

LogitBoost with simple regression functions as base learners. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.simple_logistic")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
I	integer	-		$(-\infty, \infty)$
S	logical	FALSE	TRUE, FALSE	-
P	logical	FALSE	TRUE, FALSE	-
M	integer	-		$(-\infty, \infty)$
H	integer	50		$(-\infty, \infty)$
W	numeric	0		$(-\infty, \infty)$
A	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSimpleLogistic

Methods

Public methods

LearnerClassifSimpleLogistic$new()
LearnerClassifSimpleLogistic$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifSimpleLogistic$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifSimpleLogistic$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Landwehr, Niels, Hall, Mark, Frank, Eibe (2005). “Logistic model trees.” Machine learning, 59(1), 161–205.

Sumner M, Frank E, Hall M (2005). “Speeding up Logistic Model Tree Induction.” In 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, 675-683.

Examples


# Define the Learner
learner = mlr3::lrn("classif.simple_logistic")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.simple_logistic")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Support Vector Machine Learner

Description

Support Vector classifier trained with John Platt's sequential minimal optimization algorithm. Calls RWeka::SMO() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
E_poly:
- original id: E
L_poly:
- original id: L
C_poly:
- original id: C
C_logistic:
- original id: C
R_logistic:
- original id: R
M_logistic:
- original id: M
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.smo")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
no_checks	logical	FALSE	TRUE, FALSE	-
C	numeric	1		$(-\infty, \infty)$
N	character	0	0, 1, 2	-
L	numeric	0.001		$(-\infty, \infty)$
P	numeric	1e-12		$(-\infty, \infty)$
M	logical	FALSE	TRUE, FALSE	-
V	integer	-1		$(-\infty, \infty)$
W	integer	1		$(-\infty, \infty)$
K	character	PolyKernel	NormalizedPolyKernel, PolyKernel, Puk, RBFKernel, StringKernel	-
calibrator	untyped	"weka.classifiers.functions.Logistic"		-
E_poly	numeric	1		$(-\infty, \infty)$
L_poly	logical	FALSE	TRUE, FALSE	-
C_poly	integer	25007		$(-\infty, \infty)$
C_logistic	logical	FALSE	TRUE, FALSE	-
R_logistic	numeric	-		$(-\infty, \infty)$
M_logistic	integer	-1		$(-\infty, \infty)$
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifSMO

Methods

Public methods

LearnerClassifSMO$new()
LearnerClassifSMO$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifSMO$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifSMO$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Platt J (1998). “Fast Training of Support Vector Machines using Sequential Minimal Optimization.” In Schoelkopf B, Burges C, Smola A (eds.), Advances in Kernel Methods - Support Vector Learning. MIT Press. http://research.microsoft.com/jplatt/smo.html.

Keerthi S, Shevade S, Bhattacharyya C, Murthy K (2001). “Improvements to Platt's SMO Algorithm for SVM Classifier Design.” Neural Computation, 13(3), 637-649.

Hastie T, Tibshirani R (1998). “Classification by Pairwise Coupling.” In Jordan MI, Kearns MJ, Solla SA (eds.), Advances in Neural Information Processing Systems, volume 10.

Examples


# Define the Learner
learner = mlr3::lrn("classif.smo")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.smo")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Classification Voted Perceptron Learner

Description

Voted Perceptron Algorithm by Freund and Schapire. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.voted_perceptron")

Meta Information

Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
I	integer	1		$[1, \infty)$
E	numeric	1		$(-\infty, \infty)$
S	integer	1		$(-\infty, \infty)$
M	integer	10000		$(-\infty, \infty)$
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifVotedPerceptron

Methods

Public methods

LearnerClassifVotedPerceptron$new()
LearnerClassifVotedPerceptron$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerClassifVotedPerceptron$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerClassifVotedPerceptron$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Freund Y, Schapire RE (1998). “Large margin classification using the perceptron algorithm.” In 11th Annual Conference on Computational Learning Theory, 209-217.

Examples


# Define the Learner
learner = mlr3::lrn("classif.voted_perceptron")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("classif.voted_perceptron")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Density KS Kernel Learner

Description

Kernel density estimator. Calls ks::kde() from ks.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.kde_ks")

Meta Information

Task type: “dens”
Predict Types: “pdf”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, ks

Parameters

Id	Type	Default	Levels	Range
h	numeric	-		$[0, \infty)$
H	untyped	-		-
gridsize	untyped	-		-
gridtype	untyped	-		-
xmin	numeric	-		$(-\infty, \infty)$
xmax	numeric	-		$(-\infty, \infty)$
supp	numeric	3.7		$(-\infty, \infty)$
binned	numeric	-		$(-\infty, \infty)$
bgridsize	untyped	-		-
positive	logical	FALSE	TRUE, FALSE	-
adj.positive	untyped	-		-
w	untyped	-		-
compute.cont	logical	TRUE	TRUE, FALSE	-
approx.cont	logical	TRUE	TRUE, FALSE	-
unit.interval	logical	FALSE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-
density	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensKDEks

Methods

Public methods

LearnerDensKDEks$new()
LearnerDensKDEks$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerDensKDEks$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerDensKDEks$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Gramacki, Artur, Gramacki, Jarosław (2017). “FFT-based fast computation of multivariate kernel density estimators with unconstrained bandwidth matrices.” Journal of Computational and Graphical Statistics, 26(2), 459–462.

Examples


# Define the Learner
learner = mlr3::lrn("dens.kde_ks")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("dens.kde_ks")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Density Locfit Learner

Description

Local density estimation. Calls locfit::density.lf() from locfit.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.locfit")

Meta Information

Task type: “dens”
Predict Types: “pdf”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, locfit

Parameters

Id	Type	Default	Levels	Range
window	character	gaus	tcub, rect, trwt, tria, epan, bisq, gaus	-
width	numeric	-		$(-\infty, \infty)$
from	numeric	-		$(-\infty, \infty)$
to	numeric	-		$(-\infty, \infty)$
cut	numeric	-		$(-\infty, \infty)$
deg	numeric	0		$(-\infty, \infty)$
link	character	ident	ident, log, logit, inverse, sqrt, arcsin	-
kern	character	tcub	rect, trwt, tria, epan, bisq, gauss, tcub	-
kt	character	sph	sph, prod	-
renorm	logical	FALSE	TRUE, FALSE	-
maxk	integer	100		$[0, \infty)$
itype	character	-	prod, mult, mlin, haz	-
mint	integer	20		$[1, \infty)$
maxit	integer	20		$[1, \infty)$

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensLocfit

Methods

Public methods

LearnerDensLocfit$new()
LearnerDensLocfit$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerDensLocfit$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerDensLocfit$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Loader, Clive (2006). Local regression and likelihood. Springer Science & Business Media.

Examples


# Define the Learner
learner = mlr3::lrn("dens.locfit")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("dens.locfit")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Density Logspline Learner

Description

Density logspline learner. Calls logspline::logspline() from logspline.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.logspline")

Meta Information

Task type: “dens”
Predict Types: “pdf”, “cdf”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, logspline

Parameters

Id	Type	Default	Levels	Range
lbound	numeric	-		$(-\infty, \infty)$
ubound	numeric	-		$(-\infty, \infty)$
maxknots	numeric	0		$[0, \infty)$
knots	untyped	-		-
nknots	numeric	0		$[0, \infty)$
penalty	untyped	-		-
silent	logical	TRUE	TRUE, FALSE	-
mind	numeric	-1		$(-\infty, \infty)$
error.action	integer	2		$[0, 2]$

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensLogspline

Methods

Public methods

LearnerDensLogspline$new()
LearnerDensLogspline$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerDensLogspline$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerDensLogspline$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Kooperberg, Charles, Stone, J C (1992). “Logspline density estimation for censored data.” Journal of Computational and Graphical Statistics, 1(4), 301–328.

Examples


# Define the Learner
learner = mlr3::lrn("dens.logspline")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("dens.logspline")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Density Mixed Data Kernel Learner

Description

Density estimator for discrete and continuous variables. Calls np::npudens() from np.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.mixed")

Meta Information

Task type: “dens”
Predict Types: “pdf”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, np

Parameters

Id	Type	Default	Levels	Range
bws	untyped	-		-
ckertype	character	gaussian	gaussian, epanechnikov, uniform	-
bwscaling	logical	FALSE	TRUE, FALSE	-
bwmethod	character	cv.ml	cv.ml, cv.ls, normal-reference	-
bwtype	character	fixed	fixed, generalized_nn, adaptive_nn	-
bandwidth.compute	logical	FALSE	TRUE, FALSE	-
ckerorder	integer	2		$[2, 8]$
remin	logical	TRUE	TRUE, FALSE	-
itmax	integer	10000		$[1, \infty)$
nmulti	integer	-		$[1, \infty)$
ftol	numeric	1.490116e-07		$(-\infty, \infty)$
tol	numeric	0.0001490116		$(-\infty, \infty)$
small	numeric	1.490116e-05		$(-\infty, \infty)$
lbc.dir	numeric	0.5		$(-\infty, \infty)$
dfc.dir	numeric	0.5		$(-\infty, \infty)$
cfac.dir	untyped	2.5 * (3 - sqrt(5))		-
initc.dir	numeric	1		$(-\infty, \infty)$
lbd.dir	numeric	0.1		$(-\infty, \infty)$
hbd.dir	numeric	1		$(-\infty, \infty)$
dfac.dir	untyped	0.25 * (3 - sqrt(5))		-
initd.dir	numeric	1		$(-\infty, \infty)$
lbc.init	numeric	0.1		$(-\infty, \infty)$
hbc.init	numeric	2		$(-\infty, \infty)$
cfac.init	numeric	0.5		$(-\infty, \infty)$
lbd.init	numeric	0.1		$(-\infty, \infty)$
hbd.init	numeric	0.9		$(-\infty, \infty)$
dfac.init	numeric	0.37		$(-\infty, \infty)$
ukertype	character	-	aitchisonaitken, liracine	-
okertype	character	-	wangvanryzin, liracine	-

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensMixed

Methods

Public methods

LearnerDensMixed$new()
LearnerDensMixed$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerDensMixed$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerDensMixed$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Li, Qi, Racine, Jeff (2003). “Nonparametric estimation of distributions with categorical and continuous data.” journal of multivariate analysis, 86(2), 266–292.

Examples


# Define the Learner
learner = mlr3::lrn("dens.mixed")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("dens.mixed")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Density Nonparametric Learner

Description

Nonparametric density estimation. Calls sm::sm.density() from sm.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.nonpar")

Meta Information

Task type: “dens”
Predict Types: “pdf”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, sm

Parameters

Id	Type	Default	Levels	Range
h	numeric	-		$(-\infty, \infty)$
group	untyped	-		-
delta	numeric	-		$(-\infty, \infty)$
h.weights	numeric	1		$(-\infty, \infty)$
hmult	untyped	1		-
method	character	normal	normal, cv, sj, df, aicc	-
positive	logical	FALSE	TRUE, FALSE	-
verbose	untyped	1		-

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensNonparametric

Methods

Public methods

LearnerDensNonparametric$new()
LearnerDensNonparametric$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerDensNonparametric$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerDensNonparametric$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Bowman, A.W., Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations, series Oxford Statistical Science Series. OUP Oxford. ISBN 9780191545696, https://books.google.de/books?id=7WBMrZ9umRYC.

Examples


# Define the Learner
learner = mlr3::lrn("dens.nonpar")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("dens.nonpar")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Density Penalized Learner

Description

Density estimation using penalized B-splines with automatic selection of smoothing parameter. Calls pendensity::pendensity() from pendensity.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.pen")

Meta Information

Task type: “dens”
Predict Types: “pdf”, “cdf”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, pendensity

Parameters

Id	Type	Default	Levels	Range
base	character	bspline	bspline, gaussian	-
no.base	numeric	41		$(-\infty, \infty)$
max.iter	numeric	20		$(-\infty, \infty)$
lambda0	numeric	500		$(-\infty, \infty)$
q	numeric	3		$(-\infty, \infty)$
sort	logical	TRUE	TRUE, FALSE	-
with.border	untyped	-		-
m	numeric	3		$(-\infty, \infty)$
eps	numeric	0.01		$(-\infty, \infty)$

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensPenalized

Methods

Public methods

LearnerDensPenalized$new()
LearnerDensPenalized$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerDensPenalized$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerDensPenalized$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Schellhase, Christian, Kauermann, Göran (2012). “Density estimation and comparison with a penalized mixture approach.” Computational Statistics, 27(4), 757–777.

Examples


# Define the Learner
learner = mlr3::lrn("dens.pen")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("dens.pen")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Density Plug-In Kernel Learner

Description

Kernel density estimation with global bandwidth selection via "plug-in". Calls plugdensity::plugin.density() from plugdensity.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.plug")

Meta Information

Task type: “dens”
Predict Types: “pdf”
Feature Types: “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, plugdensity

Parameters

Id	Type	Default	Levels
na.rm	logical	FALSE	TRUE, FALSE

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensPlugin

Methods

Public methods

LearnerDensPlugin$new()
LearnerDensPlugin$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerDensPlugin$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerDensPlugin$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Engel, Joachim, Herrmann, Eva, Gasser, Theo (1994). “An iterative bandwidth selector for kernel estimation of densities and their derivatives.” Journaltitle of Nonparametric Statistics, 4(1), 21–34.

Examples


# Define the Learner
learner = mlr3::lrn("dens.plug")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("dens.plug")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Density Smoothing Splines Learner

Description

Density Smoothing Splines Learner. Calls gss::ssden() from gss.

Dictionary

This Learner can be instantiated via lrn():

lrn("dens.spline")

Meta Information

Task type: “dens”
Predict Types: “pdf”, “cdf”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, gss

Parameters

Id	Type	Default	Levels	Range
type	untyped	-		-
alpha	numeric	1.4		$(-\infty, \infty)$
weights	untyped	-		-
na.action	untyped	stats::na.omit		-
id.basis	untyped	-		-
nbasis	integer	-		$(-\infty, \infty)$
seed	numeric	-		$(-\infty, \infty)$
domain	untyped	-		-
quad	untyped	-		-
qdsz.depth	numeric	-		$(-\infty, \infty)$
bias	untyped	-		-
prec	numeric	1e-07		$(-\infty, \infty)$
maxiter	integer	30		$[1, \infty)$
skip.iter	logical	-	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensSpline

Methods

Public methods

LearnerDensSpline$new()
LearnerDensSpline$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerDensSpline$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerDensSpline$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Gu, Chong, Wang, Jingyuan (2003). “Penalized likelihood density estimation: Direct cross-validation and scalable approximation.” Statistica Sinica, 811–826.

Examples


# Define the Learner
learner = mlr3::lrn("dens.spline")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("dens.spline")
print(learner)

# Define a Task
task = mlr3::tsk("faithful")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Abess Learner

Description

Adaptive best-subset selection for regression. Calls abess::abess() from abess.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.abess")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, abess

Parameters

Id	Type	Default	Levels	Range
family	character	gaussian	gaussian, mgaussian, poisson, gamma	-
tune.path	character	sequence	sequence, gsection	-
tune.type	character	gic	gic, aic, bic, ebic, cv	-
normalize	integer	NULL		$(-\infty, \infty)$
support.size	untyped	NULL		-
c.max	integer	2		$[1, \infty)$
gs.range	untyped	NULL		-
lambda	numeric	0		$[0, \infty)$
always.include	untyped	NULL		-
group.index	untyped	NULL		-
init.active.set	untyped	NULL		-
splicing.type	integer	2		$[1, 2]$
max.splicing.iter	integer	20		$[1, \infty)$
screening.num	integer	NULL		$[0, \infty)$
important.search	integer	NULL		$[0, \infty)$
warm.start	logical	TRUE	TRUE, FALSE	-
nfolds	integer	5		$(-\infty, \infty)$
foldid	untyped	NULL		-
cov.update	logical	FALSE	TRUE, FALSE	-
newton	character	exact	exact, approx	-
newton.thresh	numeric	1e-06		$[0, \infty)$
max.newton.iter	integer	NULL		$[1, \infty)$
early.stop	logical	FALSE	TRUE, FALSE	-
ic.scale	numeric	1		$[0, \infty)$
num.threads	integer	0		$[0, \infty)$
seed	integer	1		$(-\infty, \infty)$

Initial parameter values

num.threads: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.
family: Depends on the task type, if the parameter family is NULL, it is set to "binomial" for binary classification tasks and to "multinomial" for multiclass classification problems.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrAbess

Methods

Public methods

LearnerRegrAbess$new()
LearnerRegrAbess$selected_features()
LearnerRegrAbess$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrAbess$new()

Method `selected_features()`

Extract the name of selected features from the model by abess::extract().

Usage

LearnerRegrAbess$selected_features()

Returns

The names of selected features

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrAbess$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

abess-team

Examples


# Define the Learner
learner = mlr3::lrn("regr.abess")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.abess")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression BART (Bayesian Additive Regression Trees) Learner

Description

Bayesian Additive Regression Trees are similar to gradient boosting algorithms. Calls dbarts::bart() from dbarts.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.bart")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, dbarts

Parameters

Id	Type	Default	Levels	Range
ntree	integer	200		$[1, \infty)$
sigest	untyped	NULL		-
sigdf	integer	3		$[1, \infty)$
sigquant	numeric	0.9		$[0, 1]$
k	numeric	2		$[0, \infty)$
power	numeric	2		$[0, \infty)$
base	numeric	0.95		$[0, 1]$
ndpost	integer	1000		$[1, \infty)$
nskip	integer	100		$[0, \infty)$
printevery	integer	100		$[0, \infty)$
keepevery	integer	1		$[1, \infty)$
keeptrainfits	logical	TRUE	TRUE, FALSE	-
usequants	logical	FALSE	TRUE, FALSE	-
numcut	integer	100		$[1, \infty)$
printcutoffs	integer	0		$(-\infty, \infty)$
verbose	logical	FALSE	TRUE, FALSE	-
nthread	integer	1		$(-\infty, \infty)$
keeptrees	logical	FALSE	TRUE, FALSE	-
keepcall	logical	TRUE	TRUE, FALSE	-
sampleronly	logical	FALSE	TRUE, FALSE	-
seed	integer	NA		$(-\infty, \infty)$
proposalprobs	untyped	NULL		-
splitprobs	untyped	NULL		-
keepsampler	logical	-	TRUE, FALSE	-

Custom mlr3 parameters

Parameter: offset
- The parameter is removed, because only dbarts::bart2 allows an offset during training, and therefore the offset parameter in dbarts:::predict.bart is irrelevant for dbarts::dbart.
Parameter: nchain, combineChains, combinechains
- The parameters are removed as parallelization of multiple models is handled by future.

Initial parameter values

keeptrees is initialized to TRUE because it is required for prediction.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrBart

Methods

Public methods

LearnerRegrBart$new()
LearnerRegrBart$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrBart$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrBart$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

ck37

References

Chipman, A H, George, I E, McCulloch, E R (2010). “BART: Bayesian additive regression trees.” The Annals of Applied Statistics, 4(1), 266–298.

Examples


# Define the Learner
learner = mlr3::lrn("regr.bart")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.bart")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Gradient Boosted Decision Trees Regression Learner

Description

Gradient boosting algorithm that also supports categorical data. Calls catboost::catboost.train() from package 'catboost'.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.catboost")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, catboost

Parameters

Id	Type	Default	Levels	Range
loss_function	character	RMSE	MAE, MAPE, Poisson, Quantile, RMSE, LogLinQuantile, Lq, Huber, Expectile, Tweedie	-
learning_rate	numeric	0.03		$[0.001, 1]$
random_seed	integer	0		$[0, \infty)$
l2_leaf_reg	numeric	3		$[0, \infty)$
bootstrap_type	character	-	Bayesian, Bernoulli, MVS, Poisson, No	-
bagging_temperature	numeric	1		$[0, \infty)$
subsample	numeric	-		$[0, 1]$
sampling_frequency	character	PerTreeLevel	PerTree, PerTreeLevel	-
sampling_unit	character	Object	Object, Group	-
mvs_reg	numeric	-		$[0, \infty)$
random_strength	numeric	1		$[0, \infty)$
depth	integer	6		$[1, 16]$
grow_policy	character	SymmetricTree	SymmetricTree, Depthwise, Lossguide	-
min_data_in_leaf	integer	1		$[1, \infty)$
max_leaves	integer	31		$[1, \infty)$
has_time	logical	FALSE	TRUE, FALSE	-
rsm	numeric	1		$[0.001, 1]$
nan_mode	character	Min	Min, Max	-
fold_permutation_block	integer	-		$[1, 256]$
leaf_estimation_method	character	-	Newton, Gradient, Exact	-
leaf_estimation_iterations	integer	-		$[1, \infty)$
leaf_estimation_backtracking	character	AnyImprovement	No, AnyImprovement, Armijo	-
fold_len_multiplier	numeric	2		$[1.001, \infty)$
approx_on_full_history	logical	TRUE	TRUE, FALSE	-
boosting_type	character	-	Ordered, Plain	-
boost_from_average	logical	-	TRUE, FALSE	-
langevin	logical	FALSE	TRUE, FALSE	-
diffusion_temperature	numeric	10000		$[0, \infty)$
score_function	character	Cosine	Cosine, L2, NewtonCosine, NewtonL2	-
monotone_constraints	untyped	-		-
feature_weights	untyped	-		-
first_feature_use_penalties	untyped	-		-
penalties_coefficient	numeric	1		$[0, \infty)$
per_object_feature_penalties	untyped	-		-
model_shrink_rate	numeric	-		$(-\infty, \infty)$
model_shrink_mode	character	-	Constant, Decreasing	-
target_border	numeric	-		$(-\infty, \infty)$
border_count	integer	-		$[1, 65535]$
feature_border_type	character	GreedyLogSum	Median, Uniform, UniformAndQuantiles, MaxLogSum, MinEntropy, GreedyLogSum	-
per_float_feature_quantization	untyped	-		-
thread_count	integer	1		$[-1, \infty)$
task_type	character	CPU	CPU, GPU	-
devices	untyped	-		-
logging_level	character	Silent	Silent, Verbose, Info, Debug	-
metric_period	integer	1		$[1, \infty)$
train_dir	untyped	"catboost_info"		-
model_size_reg	numeric	0.5		$[0, 1]$
allow_writing_files	logical	FALSE	TRUE, FALSE	-
save_snapshot	logical	FALSE	TRUE, FALSE	-
snapshot_file	untyped	-		-
snapshot_interval	integer	600		$[1, \infty)$
simple_ctr	untyped	-		-
combinations_ctr	untyped	-		-
ctr_target_border_count	integer	-		$[1, 255]$
counter_calc_method	character	Full	SkipTest, Full	-
max_ctr_complexity	integer	-		$[1, \infty)$
ctr_leaf_count_limit	integer	-		$[1, \infty)$
store_all_simple_ctr	logical	FALSE	TRUE, FALSE	-
final_ctr_computation_mode	character	Default	Default, Skip	-
verbose	logical	FALSE	TRUE, FALSE	-
ntree_start	integer	0		$[0, \infty)$
ntree_end	integer	0		$[0, \infty)$
early_stopping_rounds	integer	-		$[1, \infty)$
eval_metric	untyped	-		-
use_best_model	logical	-	TRUE, FALSE	-
iterations	integer	1000		$[1, \infty)$

Installation

See https://catboost.ai/en/docs/concepts/r-installation.

Initial parameter values

logging_level:
- Actual default: "Verbose"
- Adjusted default: "Silent"
- Reason for change: consistent with other mlr3 learners
thread_count:
- Actual default: -1
- Adjusted default: 1
- Reason for change: consistent with other mlr3 learners
allow_writing_files:
- Actual default: TRUE
- Adjusted default: FALSE
- Reason for change: consistent with other mlr3 learners
save_snapshot:
- Actual default: TRUE
- Adjusted default: FALSE
- Reason for change: consistent with other mlr3 learners

Early stopping

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCatboost

Active bindings

internal_valid_scores: The last observation of the validation scores for all metrics. Extracted from model$evaluation_log
internal_tuned_values: Returns the early stopped iterations if early_stopping_rounds was set during training.
validate: How to construct the internal validation data. This parameter can be either NULL, a ratio, "test", or "predefined".

Methods

Public methods

LearnerRegrCatboost$new()
LearnerRegrCatboost$importance()
LearnerRegrCatboost$clone()

Inherited methods

Method `new()`

Create a LearnerRegrCatboost object.

Usage

LearnerRegrCatboost$new()

Method `importance()`

The importance scores are calculated using catboost.get_feature_importance, setting type = "FeatureImportance", returned for 'all'.

Usage

LearnerRegrCatboost$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrCatboost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Dorogush, Veronika A, Ershov, Vasily, Gulin, Andrey (2018). “CatBoost: gradient boosting with categorical features support.” arXiv preprint arXiv:1810.11363.

Examples


# Define the Learner
learner = mlr3::lrn("regr.catboost")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.catboost")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Conditional Random Forest Learner

Description

A random forest based on conditional inference trees (ctree). Calls partykit::cforest() from partykit.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.cforest")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		$[1, \infty)$
replace	logical	FALSE	TRUE, FALSE	-
fraction	numeric	0.632		$[0, 1]$
mtry	integer	-		$[0, \infty)$
mtryratio	numeric	-		$[0, 1]$
applyfun	untyped	-		-
cores	integer	NULL		$(-\infty, \infty)$
trace	logical	FALSE	TRUE, FALSE	-
offset	untyped	-		-
cluster	untyped	-		-
scores	untyped	-		-
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Univariate	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
pargs	untyped	-		-
alpha	numeric	0.05		$[0, 1]$
mincriterion	numeric	0		$[0, 1]$
logmincriterion	numeric	0		$(-\infty, \infty)$
minsplit	integer	20		$[1, \infty)$
minbucket	integer	7		$[1, \infty)$
minprob	numeric	0.01		$[0, 1]$
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
maxvar	integer	-		$[1, \infty)$
nresample	integer	9999		$[1, \infty)$
tol	numeric	1.490116e-08		$[0, \infty)$
maxsurrogate	integer	0		$[0, \infty)$
numsurrogate	logical	FALSE	TRUE, FALSE	-
maxdepth	integer	Inf		$[0, \infty)$
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		$[0, \infty)$
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
saveinfo	logical	FALSE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
OOB	logical	FALSE	TRUE, FALSE	-
simplify	logical	TRUE	TRUE, FALSE	-
scale	logical	TRUE	TRUE, FALSE	-
nperm	integer	1		$[0, \infty)$
risk	character	loglik	loglik, misclassification	-
conditional	logical	FALSE	TRUE, FALSE	-
threshold	numeric	0.2		$(-\infty, \infty)$

Custom mlr3 parameters

mtry:
- This hyperparameter can alternatively be set via the added hyperparameter mtryratio as mtry = max(ceiling(mtryratio * n_features), 1). Note that mtry and mtryratio are mutually exclusive.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCForest

Methods

Public methods

LearnerRegrCForest$new()
LearnerRegrCForest$oob_error()
LearnerRegrCForest$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrCForest$new()

Method `oob_error()`

The out-of-bag error, calculated using the OOB predictions from partykit.

Usage

LearnerRegrCForest$oob_error()

Returns

numeric(1).

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrCForest$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Examples


# Define the Learner
learner = mlr3::lrn("regr.cforest")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.cforest")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Conditional Inference Tree Learner

Description

Regression Partition Tree where a significance test is used to determine the univariate splits. Calls partykit::ctree() from partykit.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.ctree")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin

Parameters

Id	Type	Default	Levels	Range
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Bonferroni	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
alpha	numeric	0.05		$[0, 1]$
mincriterion	numeric	0.95		$[0, 1]$
logmincriterion	numeric	-		$(-\infty, \infty)$
minsplit	integer	20		$[1, \infty)$
minbucket	integer	7		$[1, \infty)$
minprob	numeric	0.01		$[0, \infty)$
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
maxvar	integer	-		$[1, \infty)$
nresample	integer	9999		$[1, \infty)$
tol	numeric	-		$[0, \infty)$
maxsurrogate	integer	0		$[0, \infty)$
numsurrogate	logical	FALSE	TRUE, FALSE	-
mtry	integer	Inf		$[0, \infty)$
maxdepth	integer	Inf		$[0, \infty)$
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		$[0, \infty)$
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	FALSE	TRUE, FALSE	-
applyfun	untyped	-		-
cores	integer	NULL		$(-\infty, \infty)$
saveinfo	logical	TRUE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
offset	untyped	-		-
cluster	untyped	-		-
scores	untyped	-		-
doFit	logical	TRUE	TRUE, FALSE	-
maxpts	integer	25000		$(-\infty, \infty)$
abseps	numeric	0.001		$[0, \infty)$
releps	numeric	0		$[0, \infty)$

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCTree

Methods

Public methods

LearnerRegrCTree$new()
LearnerRegrCTree$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrCTree$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrCTree$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Examples


# Define the Learner
learner = mlr3::lrn("regr.ctree")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.ctree")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Cubist Learner

Description

Rule-based model that is an extension of Quinlan's M5 model tree. Each tree contains linear regression models at the terminal leaves. Calls Cubist::cubist() from Cubist.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.cubist")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, Cubist

Parameters

Id	Type	Default	Levels	Range
committees	integer	-		$[1, 100]$
unbiased	logical	FALSE	TRUE, FALSE	-
rules	integer	100		$[1, \infty)$
extrapolation	numeric	100		$[0, 100]$
sample	integer	0		$[0, \infty)$
seed	integer	-		$(-\infty, \infty)$
label	untyped	"outcome"		-
neighbors	integer	-		$[0, 9]$

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrCubist

Methods

Public methods

LearnerRegrCubist$new()
LearnerRegrCubist$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrCubist$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrCubist$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Quinlan, R J, others (1992). “Learning with continuous classes.” In 5th Australian joint conference on artificial intelligence, volume 92, 343–348. World Scientific.

Quinlan, Ross J (1993). “Combining instance-based and model-based learning.” In Proceedings of the tenth international conference on machine learning, 236–243.

Examples


# Define the Learner
learner = mlr3::lrn("regr.cubist")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.cubist")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Decision Stump Learner

Description

Decision Stump Learner. Calls RWeka::DecisionStump() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.decision_stump")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrDecisionStump

Methods

Public methods

LearnerRegrDecisionStump$new()
LearnerRegrDecisionStump$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrDecisionStump$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrDecisionStump$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("regr.decision_stump")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.decision_stump")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Decision Table Learner

Description

Simple Decision Table majority regressor. Calls RWeka::make_Weka_classifier() from RWeka.

Initial parameter values

E:
- Has only 2 out of 4 original evaluation measures : rmse and mae with rmse being the default
- Reason for change: this learner should only contain evaluation measures appropriate for regression tasks

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
P_best:
- original id: P
D_best:
- original id: D
N_best:
- original id: N
S_best:
- original id: S
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.decision_table")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
S	character	BestFirst	BestFirst, GreedyStepwise	-
X	integer	1		$(-\infty, \infty)$
E	character	rmse	rmse, mae	-
I	logical	-	TRUE, FALSE	-
R	logical	-	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
P_best	untyped	-		-
D_best	character	1	0, 1, 2	-
N_best	integer	-		$(-\infty, \infty)$
S_best	integer	1		$(-\infty, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrDecisionTable

Methods

Public methods

LearnerRegrDecisionTable$new()
LearnerRegrDecisionTable$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrDecisionTable$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrDecisionTable$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Kohavi R (1995). “The Power of Decision Tables.” In 8th European Conference on Machine Learning, 174–189.

Examples


# Define the Learner
learner = mlr3::lrn("regr.decision_table")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.decision_table")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Implementation of Multivariate Adaptive Regression Splines

Description

This is an alternative implementation of MARS (Multivariate Adaptive Regression Splines). MARS is trademarked and thus not used as the name. The name "earth" stands for "Enhanced Adaptive Regression Through Hinges".

Calls earth::earth() from earth.

Details

Methods for variance estimations are not yet implemented.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.earth")

Meta Information

Task type: “regr”
Predict Types: “response”, “se”
Feature Types: “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, earth

Parameters

Id	Type	Default	Levels	Range
wp	untyped	NULL		-
offset	untyped	NULL		-
keepxy	logical	FALSE	TRUE, FALSE	-
trace	character	0	0, .3, .5, 1, 2, 3, 4, 5	-
degree	integer	1		$[1, \infty)$
penalty	numeric	2		$[-1, \infty)$
nk	untyped	NULL		-
thresh	numeric	0.001		$(-\infty, \infty)$
minspan	numeric	0		$[0, \infty)$
endspan	numeric	0		$[0, \infty)$
newvar.penalty	numeric	0		$[0, \infty)$
fast.k	integer	20		$[0, \infty)$
fast.beta	integer	1		$[0, 1]$
linpreds	untyped	FALSE		-
allowed	untyped	-		-
pmethod	character	backward	backward, none, exhaustive, forward, seqrep, cv	-
nprune	integer	-		$[0, \infty)$
nfold	integer	0		$[0, \infty)$
ncross	integer	1		$[0, \infty)$
stratify	logical	TRUE	TRUE, FALSE	-
varmod.method	character	none	none, const, lm, rlm, earth, gam, power, power0, x.lm, x.rlm, ...	-
varmod.exponent	numeric	1		$(-\infty, \infty)$
varmod.conv	numeric	1		$[0, 1]$
varmod.clamp	numeric	0.1		$(-\infty, \infty)$
varmod.minspan	numeric	-3		$(-\infty, \infty)$
Scale.y	logical	FALSE	TRUE, FALSE	-
Adjust.endspan	numeric	2		$(-\infty, \infty)$
Auto.linpreds	logical	TRUE	TRUE, FALSE	-
Force.weights	logical	FALSE	TRUE, FALSE	-
Use.beta.cache	logical	TRUE	TRUE, FALSE	-
Force.xtx.prune	logical	FALSE	TRUE, FALSE	-
Get.leverages	logical	TRUE	TRUE, FALSE	-
Exhaustive.tol	numeric	1e-10		$(-\infty, \infty)$

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrEarth

Methods

Public methods

LearnerRegrEarth$new()
LearnerRegrEarth$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrEarth$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrEarth$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

pkopper

References

Milborrow, Stephen, Hastie, T, Tibshirani, R (2014). “Earth: multivariate adaptive regression spline models.” R package version, 3(7).

Friedman, H J (1991). “Multivariate adaptive regression splines.” The annals of statistics, 19(1), 1–67.

Examples


# Define the Learner
learner = mlr3::lrn("regr.earth")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.earth")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Fast Nearest Neighbor Search Learner

Description

Fast Nearest Neighbour Regression. Calls FNN::knn.reg() from FNN.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.fnn")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, FNN

Parameters

Id	Type	Default	Levels	Range
k	integer	1		$[1, \infty)$
algorithm	character	kd_tree	kd_tree, cover_tree, brute	-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrFNN

Methods

Public methods

LearnerRegrFNN$new()
LearnerRegrFNN$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrFNN$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrFNN$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Examples


# Define the Learner
learner = mlr3::lrn("regr.fnn")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.fnn")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Generalized Additive Model Learner

Description

Generalized additive models. Calls mgcv::gam() from package mgcv.

Formula

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.gam")

Meta Information

Task type: “regr”
Predict Types: “response”, “se”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, mgcv

Parameters

Id	Type	Default	Levels	Range
family	character	gaussian	gaussian, poisson	-
formula	untyped	-		-
offset	untyped	NULL		-
method	character	GCV.Cp	GCV.Cp, GACV.Cp, REML, P-REML, ML, P-ML	-
optimizer	untyped	c("outer", "newton")		-
scale	numeric	0		$(-\infty, \infty)$
select	logical	FALSE	TRUE, FALSE	-
knots	untyped	NULL		-
sp	untyped	NULL		-
min.sp	untyped	NULL		-
H	untyped	NULL		-
gamma	numeric	1		$[1, \infty)$
paraPen	untyped	NULL		-
G	untyped	NULL		-
in.out	untyped	NULL		-
drop.unused.levels	logical	TRUE	TRUE, FALSE	-
drop.intercept	logical	FALSE	TRUE, FALSE	-
nthreads	integer	1		$[1, \infty)$
irls.reg	numeric	0		$[0, \infty)$
epsilon	numeric	1e-07		$[0, \infty)$
maxit	integer	200		$(-\infty, \infty)$
trace	logical	FALSE	TRUE, FALSE	-
mgcv.tol	numeric	1e-07		$[0, \infty)$
mgcv.half	integer	15		$[0, \infty)$
rank.tol	numeric	1.490116e-08		$[0, \infty)$
nlm	untyped	list()		-
optim	untyped	list()		-
newton	untyped	list()		-
outerPIsteps	integer	0		$[0, \infty)$
idLinksBases	logical	TRUE	TRUE, FALSE	-
scalePenalty	logical	TRUE	TRUE, FALSE	-
efs.lspmax	integer	15		$[0, \infty)$
efs.tol	numeric	0.1		$[0, \infty)$
scale.est	character	fletcher	fletcher, pearson, deviance	-
nei	untyped	-		-
ncv.threads	integer	1		$[1, \infty)$
edge.correct	logical	FALSE	TRUE, FALSE	-
block.size	integer	1000		$(-\infty, \infty)$
unconditional	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGam

Methods

Public methods

LearnerRegrGam$new()
LearnerRegrGam$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrGam$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrGam$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

pierrecamilleri

References

Hastie, J T, Tibshirani, J R (2017). Generalized additive models. Routledge.

Wood, Simon (2012). “mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation.”

Examples


# simple example
t = mlr3::tsk("mtcars")
l = mlr3::lrn("regr.gam")
l$param_set$values$formula = mpg ~ cyl + am + s(disp) + s(hp)
l$train(t)
l$model

# simple example
t = mlr3::tsk("mtcars")
l = mlr3::lrn("regr.gam")
l$param_set$values$formula = mpg ~ cyl + am + s(disp) + s(hp)
l$train(t)
l$model

Boosted Generalized Additive Regression Learner

Description

Fit a generalized additive regression model using a boosting algorithm. Calls mboost::gamboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.gamboost")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, mboost

Parameters

Id	Type	Default	Levels	Range
baselearner	character	bbs	bbs, bols, btree	-
dfbase	integer	4		$(-\infty, \infty)$
offset	numeric	NULL		$(-\infty, \infty)$
family	character	Gaussian	Gaussian, Laplace, Huber, Poisson, GammaReg, NBinomial, Hurdle, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
d	numeric	NULL		$(-\infty, \infty)$
mstop	integer	100		$(-\infty, \infty)$
nu	numeric	0.1		$(-\infty, \infty)$
risk	character	inbag	inbag, oobag, none	-
oobweights	untyped	NULL		-
trace	logical	FALSE	TRUE, FALSE	-
stopintern	untyped	FALSE		-
na.action	untyped	stats::na.omit		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGAMBoost

Methods

Public methods

LearnerRegrGAMBoost$new()
LearnerRegrGAMBoost$clone()

Inherited methods

Method `new()`

Create a LearnerRegrGAMBoost object.

Usage

LearnerRegrGAMBoost$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrGAMBoost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

Examples

# Define the Learner
learner = lrn("regr.gamboost", baselearner = "bols")
print(learner)
# Define the Learner
learner = lrn("regr.gamboost", baselearner = "bols")
print(learner)

Regression Gaussian Processes Learner From Weka

Description

Gaussian Processes. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
E_poly:
- original id: E
L_poly:
- original id: L (duplicated L for when K is set to PolyKernel)
C_poly:
- original id: C
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
output-debug-info for kernel parameter removed:
- enables debugging output (if available) to be printed
Reason for change: The parameter is removed because it's unclear how to actually use it.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.gaussian_processes")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
L	numeric	1		$(-\infty, \infty)$
N	character	0	0, 1, 2	-
K	character	supportVector.PolyKernel	supportVector.NormalizedPolyKernel, supportVector.PolyKernel, supportVector.Puk, supportVector.RBFKernel, supportVector.StringKernel	-
S	integer	1		$(-\infty, \infty)$
E_poly	numeric	1		$(-\infty, \infty)$
L_poly	logical	FALSE	TRUE, FALSE	-
C_poly	integer	250007		$(-\infty, \infty)$
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGaussianProcesses

Methods

Public methods

LearnerRegrGaussianProcesses$new()
LearnerRegrGaussianProcesses$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrGaussianProcesses$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrGaussianProcesses$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Mackay DJ (1998). “Introduction to Gaussian Processes.”

Examples


# Define the Learner
learner = mlr3::lrn("regr.gaussian_processes")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.gaussian_processes")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Gaussian Process Learner

Description

Gaussian process for regression. Calls kernlab::gausspr() from kernlab. Parameters sigma, degree, scale, offset and order are added to make tuning kpar easier. If kpar is provided then these new parameters are ignored. If none are provided then the default "automatic" is used for kpar.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.gausspr")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

Id	Type	Default	Levels	Range
scaled	untyped	TRUE		-
kernel	character	rbfdot	rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot	-
sigma	numeric	-		$(-\infty, \infty)$
degree	numeric	-		$(-\infty, \infty)$
scale	numeric	-		$(-\infty, \infty)$
offset	numeric	-		$(-\infty, \infty)$
order	numeric	-		$(-\infty, \infty)$
kpar	untyped	"automatic"		-
var	numeric	0.001		$[0.001, \infty)$
variance.model	logical	FALSE	TRUE, FALSE	-
tol	numeric	0.001		$[0, \infty)$
fit	logical	TRUE	TRUE, FALSE	-
na.action	untyped	na.omit		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGausspr

Methods

Public methods

LearnerRegrGausspr$new()
LearnerRegrGausspr$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrGausspr$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrGausspr$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1–20.

Examples


# Define the Learner
learner = mlr3::lrn("regr.gausspr")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.gausspr")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Gradient Boosting Machine Learner

Description

Gradient Boosting Regression Algorithm. Calls gbm::gbm() from gbm.

Weights are ignored for quantile prediction.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.gbm")

Meta Information

Task type: “regr”
Predict Types: “response”, “quantiles”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, gbm

Parameters

Id	Type	Default	Levels	Range
distribution	character	gaussian	gaussian, laplace, poisson, tdist	-
n.trees	integer	100		$[1, \infty)$
interaction.depth	integer	1		$[1, \infty)$
n.minobsinnode	integer	10		$[1, \infty)$
shrinkage	numeric	0.001		$[0, \infty)$
bag.fraction	numeric	0.5		$[0, 1]$
train.fraction	numeric	1		$[0, 1]$
cv.folds	integer	0		$(-\infty, \infty)$
keep.data	logical	FALSE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-
n.cores	integer	1		$(-\infty, \infty)$
var.monotone	untyped	-		-

Parameter changes

keep.data:
- Actual default: TRUE
- Adjusted default: FALSE
- Reason for change: keep.data = FALSE saves memory during model fitting.
n.cores:
- Actual default: NULL
- Adjusted default: 1
- Reason for change: Suppressing the automatic internal parallelization if cv.folds > 0.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGBM

Methods

Public methods

LearnerRegrGBM$new()
LearnerRegrGBM$importance()
LearnerRegrGBM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrGBM$new()

Method `importance()`

The importance scores are extracted by gbm::relative.influence() from the model.

Usage

LearnerRegrGBM$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrGBM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Friedman, H J (2002). “Stochastic gradient boosting.” Computational statistics & data analysis, 38(4), 367–378.

Examples

# Define the Learner
learner = lrn("regr.gbm")
print(learner)
# Define the Learner
learner = lrn("regr.gbm")
print(learner)

Generalized Linear Regression

Description

Generalized linear model. Calls stats::glm() from base package 'stats'. For logistic regression please use mlr_learners_classif.log_reg.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.glm")

Meta Information

Task type: “regr”
Predict Types: “response”, “se”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, 'stats'

Parameters

Id	Type	Default	Levels	Range
singular.ok	logical	TRUE	TRUE, FALSE	-
x	logical	FALSE	TRUE, FALSE	-
y	logical	TRUE	TRUE, FALSE	-
model	logical	TRUE	TRUE, FALSE	-
etastart	untyped	-		-
mustart	untyped	-		-
start	untyped	NULL		-
offset	untyped	-		-
family	character	gaussian	gaussian, poisson, quasipoisson, Gamma, inverse.gaussian	-
na.action	character	-	na.omit, na.pass, na.fail, na.exclude	-
link	character	-	logit, probit, cauchit, cloglog, identity, log, sqrt, 1/mu^2, inverse	-
epsilon	numeric	1e-08		$(-\infty, \infty)$
maxit	numeric	25		$(-\infty, \infty)$
trace	logical	FALSE	TRUE, FALSE	-
dispersion	untyped	NULL		-
type	character	link	response, link, terms	-

Initial parameter values

type
- Actual default: "link"
- Adjusted default: "response"
- Reason for change: Response scale more natural for predictions.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGlm

Methods

Public methods

LearnerRegrGlm$new()
LearnerRegrGlm$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrGlm$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrGlm$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

salauer

References

Hosmer Jr, W D, Lemeshow, Stanley, Sturdivant, X R (2013). Applied logistic regression, volume 398. John Wiley & Sons.

Examples


# Define the Learner
learner = mlr3::lrn("regr.glm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.glm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Boosted Generalized Linear Regression Learner

Description

Fit a generalized linear regression model using a boosting algorithm. Calls mboost::glmboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.glmboost")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, mboost

Parameters

Id	Type	Default	Levels	Range
offset	numeric	NULL		$(-\infty, \infty)$
family	character	Gaussian	Gaussian, Laplace, Huber, Poisson, GammaReg, NBinomial, Hurdle, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
d	numeric	NULL		$(-\infty, \infty)$
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		$(-\infty, \infty)$
nu	numeric	0.1		$(-\infty, \infty)$
risk	character	inbag	inbag, oobag, none	-
oobweights	untyped	NULL		-
trace	logical	FALSE	TRUE, FALSE	-
stopintern	untyped	FALSE		-
na.action	untyped	stats::na.omit		-
contrasts.arg	untyped	-		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrGLMBoost

Methods

Public methods

LearnerRegrGLMBoost$new()
LearnerRegrGLMBoost$clone()

Inherited methods

Method `new()`

Create a LearnerRegrGLMBoost object.

Usage

LearnerRegrGLMBoost$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrGLMBoost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

Examples


# Define the Learner
learner = mlr3::lrn("regr.glmboost")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.glmboost")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression IBk Learner

Description

Instance based algorithm: K-nearest neighbours regression. Calls RWeka::IBk() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.IBk")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
weight	character	-	I, F	-
K	integer	1		$[1, \infty)$
E	logical	FALSE	TRUE, FALSE	-
W	integer	0		$[0, \infty)$
X	logical	FALSE	TRUE, FALSE	-
A	character	LinearNNSearch	BallTree, CoverTree, FilteredNeighbourSearch, KDTree, LinearNNSearch	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
weight:
- original id: I and F
Reason for change: original I and F params are interdependent (I can only be TRUE when F is FALSE and vice versa). The easiest way to encode this is to combine I and F into one factor param.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrIBk

Methods

Public methods

LearnerRegrIBk$new()
LearnerRegrIBk$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrIBk$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrIBk$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Aha, W D, Kibler, Dennis, Albert, K M (1991). “Instance-based learning algorithms.” Machine learning, 6(1), 37–66.

Examples


# Define the Learner
learner = mlr3::lrn("regr.IBk")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.IBk")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression KStar Learner

Description

Instance-based regressor which differs from other instance-based learners in that it uses an entropy-based distance function. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.kstar")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
B	integer	20		$(-\infty, \infty)$
E	logical	-	TRUE, FALSE	-
M	character	a	a, d, m, n	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrKStar

Methods

Public methods

LearnerRegrKStar$new()
LearnerRegrKStar$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrKStar$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrKStar$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Cleary JG, Trigg LE (1995). “K*: An Instance-based Learner Using an Entropic Distance Measure.” In 12th International Conference on Machine Learning, 108-114.

Examples


# Define the Learner
learner = mlr3::lrn("regr.kstar")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.kstar")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Kernlab Support Vector Machine

Description

Support Vector Regression. Calls kernlab::ksvm() from kernlab.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.ksvm")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

Id	Type	Default	Levels	Range
scaled	logical	TRUE	TRUE, FALSE	-
type	character	eps-svr	eps-svr, nu-svr, eps-bsvr	-
kernel	character	rbfdot	rbfdot, polydot, vanilladot, laplacedot, besseldot, anovadot	-
C	numeric	1		$(-\infty, \infty)$
nu	numeric	0.2		$[0, \infty)$
epsilon	numeric	0.1		$(-\infty, \infty)$
cache	integer	40		$[1, \infty)$
tol	numeric	0.001		$[0, \infty)$
shrinking	logical	TRUE	TRUE, FALSE	-
sigma	numeric	-		$[0, \infty)$
degree	integer	-		$[1, \infty)$
scale	numeric	-		$[0, \infty)$
order	integer	-		$(-\infty, \infty)$
offset	numeric	-		$(-\infty, \infty)$
na.action	untyped	na.omit		-
fit	logical	TRUE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrKSVM

Methods

Public methods

LearnerRegrKSVM$new()
LearnerRegrKSVM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrKSVM$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrKSVM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

mboecker

References

Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1–20.

Examples


# Define the Learner
learner = mlr3::lrn("regr.ksvm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.ksvm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

L2-Regularized Support Vector Regression Learner

Description

L2 regularized support vector regression. Calls LiblineaR::LiblineaR() from LiblineaR.

Details

Type of SVR depends on type argument:

type = 11 - L2-regularized L2-loss support vector regression (primal)
type = 12 – L2-regularized L2-loss support vector regression (dual)
type = 13 – L2-regularized L1-loss support vector regression (dual)

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.liblinear")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, LiblineaR

Parameters

Id	Type	Default	Levels	Range
type	integer	11		$[11, 13]$
cost	numeric	1		$[0, \infty)$
bias	numeric	1		$(-\infty, \infty)$
svr_eps	numeric	NULL		$[0, \infty)$
cross	integer	0		$[0, \infty)$
verbose	logical	FALSE	TRUE, FALSE	-
findC	logical	FALSE	TRUE, FALSE	-
useInitC	logical	TRUE	TRUE, FALSE	-

Initial parameter values

svr_eps:
- Actual default: NULL
- Initial value: 0.001
- Reason for change: svr_eps is type dependent and the "type" is handled by the mlr3learner. The default value is set to th default of the respective "type".

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLiblineaR

Methods

Public methods

LearnerRegrLiblineaR$new()
LearnerRegrLiblineaR$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrLiblineaR$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrLiblineaR$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Examples


# Define the Learner
learner = mlr3::lrn("regr.liblinear")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.liblinear")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression LightGBM Learner

Description

Gradient boosting algorithm. Calls lightgbm::lightgbm() from lightgbm. The list of parameters can be found here and in the documentation of lightgbm::lgb.train().

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.lightgbm")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, lightgbm

Parameters

Id	Type	Default	Levels	Range
objective	character	regression	regression, regression_l1, huber, fair, poisson, quantile, mape, gamma, tweedie	-
eval	untyped	-		-
verbose	integer	1		$(-\infty, \infty)$
record	logical	TRUE	TRUE, FALSE	-
eval_freq	integer	1		$[1, \infty)$
callbacks	untyped	-		-
reset_data	logical	FALSE	TRUE, FALSE	-
boosting	character	gbdt	gbdt, rf, dart, goss	-
linear_tree	logical	FALSE	TRUE, FALSE	-
learning_rate	numeric	0.1		$[0, \infty)$
num_leaves	integer	31		$[1, 131072]$
tree_learner	character	serial	serial, feature, data, voting	-
num_threads	integer	0		$[0, \infty)$
device_type	character	cpu	cpu, gpu	-
seed	integer	-		$(-\infty, \infty)$
deterministic	logical	FALSE	TRUE, FALSE	-
data_sample_strategy	character	bagging	bagging, goss	-
force_col_wise	logical	FALSE	TRUE, FALSE	-
force_row_wise	logical	FALSE	TRUE, FALSE	-
histogram_pool_size	integer	-1		$(-\infty, \infty)$
max_depth	integer	-1		$(-\infty, \infty)$
min_data_in_leaf	integer	20		$[0, \infty)$
min_sum_hessian_in_leaf	numeric	0.001		$[0, \infty)$
bagging_fraction	numeric	1		$[0, 1]$
bagging_freq	integer	0		$[0, \infty)$
bagging_seed	integer	3		$(-\infty, \infty)$
bagging_by_query	logical	FALSE	TRUE, FALSE	-
feature_fraction	numeric	1		$[0, 1]$
feature_fraction_bynode	numeric	1		$[0, 1]$
feature_fraction_seed	integer	2		$(-\infty, \infty)$
extra_trees	logical	FALSE	TRUE, FALSE	-
extra_seed	integer	6		$(-\infty, \infty)$
max_delta_step	numeric	0		$(-\infty, \infty)$
lambda_l1	numeric	0		$[0, \infty)$
lambda_l2	numeric	0		$[0, \infty)$
linear_lambda	numeric	0		$[0, \infty)$
min_gain_to_split	numeric	0		$[0, \infty)$
drop_rate	numeric	0.1		$[0, 1]$
max_drop	integer	50		$(-\infty, \infty)$
skip_drop	numeric	0.5		$[0, 1]$
xgboost_dart_mode	logical	FALSE	TRUE, FALSE	-
uniform_drop	logical	FALSE	TRUE, FALSE	-
drop_seed	integer	4		$(-\infty, \infty)$
top_rate	numeric	0.2		$[0, 1]$
other_rate	numeric	0.1		$[0, 1]$
min_data_per_group	integer	100		$[1, \infty)$
max_cat_threshold	integer	32		$[1, \infty)$
cat_l2	numeric	10		$[0, \infty)$
cat_smooth	numeric	10		$[0, \infty)$
max_cat_to_onehot	integer	4		$[1, \infty)$
top_k	integer	20		$[1, \infty)$
monotone_constraints	untyped	NULL		-
monotone_constraints_method	character	basic	basic, intermediate, advanced	-
monotone_penalty	numeric	0		$[0, \infty)$
feature_contri	untyped	NULL		-
forcedsplits_filename	untyped	""		-
refit_decay_rate	numeric	0.9		$[0, 1]$
cegb_tradeoff	numeric	1		$[0, \infty)$
cegb_penalty_split	numeric	0		$[0, \infty)$
cegb_penalty_feature_lazy	untyped	-		-
cegb_penalty_feature_coupled	untyped	-		-
path_smooth	numeric	0		$[0, \infty)$
interaction_constraints	untyped	-		-
use_quantized_grad	logical	TRUE	TRUE, FALSE	-
num_grad_quant_bins	integer	4		$(-\infty, \infty)$
quant_train_renew_leaf	logical	FALSE	TRUE, FALSE	-
stochastic_rounding	logical	TRUE	TRUE, FALSE	-
serializable	logical	TRUE	TRUE, FALSE	-
max_bin	integer	255		$[2, \infty)$
max_bin_by_feature	untyped	NULL		-
min_data_in_bin	integer	3		$[1, \infty)$
bin_construct_sample_cnt	integer	200000		$[1, \infty)$
data_random_seed	integer	1		$(-\infty, \infty)$
is_enable_sparse	logical	TRUE	TRUE, FALSE	-
enable_bundle	logical	TRUE	TRUE, FALSE	-
use_missing	logical	TRUE	TRUE, FALSE	-
zero_as_missing	logical	FALSE	TRUE, FALSE	-
feature_pre_filter	logical	TRUE	TRUE, FALSE	-
pre_partition	logical	FALSE	TRUE, FALSE	-
two_round	logical	FALSE	TRUE, FALSE	-
forcedbins_filename	untyped	""		-
boost_from_average	logical	TRUE	TRUE, FALSE	-
reg_sqrt	logical	FALSE	TRUE, FALSE	-
alpha	numeric	0.9		$[0, \infty)$
fair_c	numeric	1		$[0, \infty)$
poisson_max_delta_step	numeric	0.7		$[0, \infty)$
tweedie_variance_power	numeric	1.5		$[1, 2]$
metric_freq	integer	1		$[1, \infty)$
num_machines	integer	1		$[1, \infty)$
local_listen_port	integer	12400		$[1, \infty)$
time_out	integer	120		$[1, \infty)$
machines	untyped	""		-
gpu_platform_id	integer	-1		$(-\infty, \infty)$
gpu_device_id	integer	-1		$(-\infty, \infty)$
gpu_use_dp	logical	FALSE	TRUE, FALSE	-
num_gpu	integer	1		$[1, \infty)$
start_iteration_predict	integer	0		$(-\infty, \infty)$
num_iteration_predict	integer	-1		$(-\infty, \infty)$
pred_early_stop	logical	FALSE	TRUE, FALSE	-
pred_early_stop_freq	integer	10		$(-\infty, \infty)$
pred_early_stop_margin	numeric	10		$(-\infty, \infty)$
num_iterations	integer	100		$[1, \infty)$
early_stopping_rounds	integer	-		$[1, \infty)$
early_stopping_min_delta	numeric	-		$[0, \infty)$
first_metric_only	logical	FALSE	TRUE, FALSE	-

Initial parameter values

num_threads:
- Actual default: 0L
- Initital value: 1L
- Reason for change: Prevents accidental conflicts with future.
verbose:
- Actual default: 1L
- Initial value: -1L
- Reason for change: Prevents accidental conflicts with mlr messaging system.

Early Stopping and Validation

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLightGBM

Active bindings

internal_valid_scores: The last observation of the validation scores for all metrics. Extracted from model$evaluation_log
internal_tuned_values: Returns the early stopped iterations if early_stopping_rounds was set during training.
validate: How to construct the internal validation data. This parameter can be either NULL, a ratio, "test", or "predefined".

Methods

Public methods

LearnerRegrLightGBM$new()
LearnerRegrLightGBM$importance()
LearnerRegrLightGBM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrLightGBM$new()

Method `importance()`

The importance scores are extracted from lbg.importance.

Usage

LearnerRegrLightGBM$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrLightGBM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

kapsner

References

Examples


# Define the Learner
learner = mlr3::lrn("regr.lightgbm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.lightgbm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Linear Regression Learner From Weka

Description

Linear Regression learner that uses the Akaike criterion for model selection and is able to deal with weighted instances. Calls RWeka::LinearRegression() RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
additional_stats:
- original id: additional-stats
use_qr:
- original id: use-qr
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.linear_regression")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
S	character	0	0, 1, 2	-
C	logical	FALSE	TRUE, FALSE	-
R	numeric	1e-08		$(-\infty, \infty)$
minimal	logical	FALSE	TRUE, FALSE	-
additional_stats	logical	FALSE	TRUE, FALSE	-
use_qr	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLinearRegression

Methods

Public methods

LearnerRegrLinearRegression$new()
LearnerRegrLinearRegression$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrLinearRegression$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrLinearRegression$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("regr.linear_regression")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.linear_regression")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Linear Mixed Effects Learner

Description

Linear model with random effects. Calls lme4::lmer() from lme4.

Formula

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.lmer")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, lme4

Parameters

Id	Type	Default	Levels	Range
formula	untyped	-		-
REML	logical	TRUE	TRUE, FALSE	-
start	untyped	NULL		-
verbose	integer	0		$[0, \infty)$
offset	untyped	NULL		-
contrasts	untyped	NULL		-
optimizer	character	nloptwrap	Nelder_Mead, bobyqa, nlminbwrap, nloptwrap	-
restart_edge	logical	FALSE	TRUE, FALSE	-
boundary.tol	numeric	1e-05		$[0, \infty)$
calc.derivs	logical	TRUE	TRUE, FALSE	-
check.nobs.vs.rankZ	character	ignore	ignore, warning, message, stop	-
check.nobs.vs.nlev	character	stop	ignore, warning, message, stop	-
check.nlev.gtreq.5	character	ignore	ignore, warning, message, stop	-
check.nlev.gtr.1	character	stop	ignore, warning, message, stop	-
check.nobs.vs.nRE	character	stop	ignore, warning, message, stop	-
check.rankX	character	message+drop.cols	message+drop.cols, silent.drop.cols, warn+drop.cols, stop.deficient, ignore	-
check.scaleX	character	warning	warning, stop, silent.rescale, message+rescale, warn+rescale, ignore	-
check.formula.LHS	character	stop	ignore, warning, message, stop	-
check.conv.grad	untyped	"lme4::.makeCC(\"warning\", tol = 2e-3, relTol = NULL)"		-
check.conv.singular	untyped	"lme4::.makeCC(action = \"message\", tol = formals(lme4::isSingular)$tol)"		-
check.conv.hess	untyped	"lme4::.makeCC(action = \"warning\", tol = 1e-6)"		-
optCtrl	untyped	list()		-
newparams	untyped	NULL		-
re.form	untyped	NULL		-
random.only	logical	FALSE	TRUE, FALSE	-
allow.new.levels	logical	FALSE	TRUE, FALSE	-
na.action	untyped	"stats::na.pass"		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLmer

Methods

Public methods

LearnerRegrLmer$new()
LearnerRegrLmer$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrLmer$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrLmer$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

s-kganz

References

Bates, M D (2010). “lme4: Mixed-effects modeling with R.”

Examples


# Define the Learner and set parameter values
learner = lrn("regr.lmer", formula = cmedv ~ (1 | town))

# Define a Task
task = tsk("boston_housing")

learner$train(task)
print(learner$model)

# Define the Learner and set parameter values
learner = lrn("regr.lmer", formula = cmedv ~ (1 | town))

# Define a Task
task = tsk("boston_housing")

learner$train(task)
print(learner$model)

Regression M5P Learner

Description

Implements base routines for generating M5 Model trees and rules. Calls RWeka::M5P() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.m5p")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
N	logical	-	TRUE, FALSE	-
U	logical	-	TRUE, FALSE	-
R	logical	-	TRUE, FALSE	-
M	integer	4		$(-\infty, \infty)$
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
L	logical	-	TRUE, FALSE	-
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrM5P

Methods

Public methods

LearnerRegrM5P$new()
LearnerRegrM5P$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrM5P$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrM5P$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Quinlan RJ (1992). “Learning with Continuous Classes.” In 5th Australian Joint Conference on Artificial Intelligence, 343-348.

Wang Y, Witten IH (1997). “Induction of model trees for predicting continuous classes.” In Poster papers of the 9th European Conference on Machine Learning.

Examples


# Define the Learner
learner = mlr3::lrn("regr.m5p")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.m5p")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression M5Rules Learner

Description

Algorithm for inducing decision lists from model trees. Calls RWeka::M5Rules() from RWeka.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.M5Rules")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
N	logical	FALSE	TRUE, FALSE	-
U	logical	FALSE	TRUE, FALSE	-
R	logical	FALSE	TRUE, FALSE	-
M	integer	4		$(-\infty, \infty)$
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrM5Rules

Methods

Public methods

LearnerRegrM5Rules$new()
LearnerRegrM5Rules$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrM5Rules$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrM5Rules$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

henrifnk

References

Holmes, Geoffrey, Hall, Mark, Prank, Eibe (1999). “Generating rule sets from model trees.” In Australasian joint conference on artificial intelligence, 1–12. Springer.

Examples


# Define the Learner
learner = mlr3::lrn("regr.M5Rules")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.M5Rules")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Mars Learner

Description

Multivariate Adaptive Regression Splines. Calls mda::mars() from mda.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.mars")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3extralearners, mda

Parameters

Id	Type	Default	Levels	Range
degree	integer	1		$[1, \infty)$
nk	integer	-		$[1, \infty)$
penalty	numeric	2		$[0, \infty)$
thresh	numeric	0.001		$[0, \infty)$
prune	logical	TRUE	TRUE, FALSE	-
trace.mars	logical	FALSE	TRUE, FALSE	-
forward.step	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrMars

Methods

Public methods

LearnerRegrMars$new()
LearnerRegrMars$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrMars$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrMars$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Friedman, H J (1991). “Multivariate adaptive regression splines.” The annals of statistics, 19(1), 1–67.

Examples


# Define the Learner
learner = mlr3::lrn("regr.mars")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.mars")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Model-based Recursive Partitioning Learner

Description

Model-based recursive partitioning algorithm. Calls partykit::mob() from mob.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.mob")

Meta Information

Task type: “regr”
Predict Types: “response”, “se”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, partykit, sandwich, coin

Parameters

Id	Type	Default	Levels	Range
rhs	untyped	-		-
fit	untyped	-		-
offset	untyped	-		-
cluster	untyped	-		-
alpha	numeric	0.05		$[0, 1]$
bonferroni	logical	TRUE	TRUE, FALSE	-
minsize	integer	-		$[1, \infty)$
minsplit	integer	-		$[1, \infty)$
minbucket	integer	-		$[1, \infty)$
maxdepth	integer	Inf		$[0, \infty)$
mtry	integer	Inf		$[0, \infty)$
trim	numeric	0.1		$[0, \infty)$
breakties	logical	FALSE	TRUE, FALSE	-
parm	untyped	-		-
dfsplit	integer	-		$[0, \infty)$
prune	untyped	-		-
restart	logical	TRUE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
ytype	character	vector	vector, matrix, data.frame	-
xtype	character	matrix	vector, matrix, data.frame	-
terminal	untyped	"object"		-
inner	untyped	"object"		-
model	logical	TRUE	TRUE, FALSE	-
numsplit	character	left	left, center	-
catsplit	character	binary	binary, multiway	-
vcov	character	opg	opg, info, sandwich	-
ordinal	character	chisq	chisq, max, L2	-
nrep	integer	10000		$[0, \infty)$
applyfun	untyped	-		-
cores	integer	NULL		$(-\infty, \infty)$
additional	untyped	-		-
predict_fun	untyped	-		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrMob

Methods

Public methods

LearnerRegrMob$new()
LearnerRegrMob$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrMob$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrMob$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sumny

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Examples


library(mlr3)
lm_ = function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  lm(y ~ 1, ...)
}
learner = LearnerRegrMob$new()
learner$param_set$values$rhs = "."
learner$param_set$values$fit = lm_
learner$feature_types = c("logical", "integer", "numeric", "factor", "ordered")

predict_fun = function(object, newdata, task, .type) {
  preds = predict(object, newdata = newdata, type = "response", se.fit = TRUE)
  cbind(preds$fit, preds$se.fit)
}
learner$param_set$values$predict_fun = predict_fun
task = tsk("mtcars")
ids = partition(task)
learner$train(task, row_ids = ids$train)
learner$predict(task, row_ids = ids$test)

library(mlr3)
lm_ = function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  lm(y ~ 1, ...)
}
learner = LearnerRegrMob$new()
learner$param_set$values$rhs = "."
learner$param_set$values$fit = lm_
learner$feature_types = c("logical", "integer", "numeric", "factor", "ordered")

predict_fun = function(object, newdata, task, .type) {
  preds = predict(object, newdata = newdata, type = "response", se.fit = TRUE)
  cbind(preds$fit, preds$se.fit)
}
learner$param_set$values$predict_fun = predict_fun
task = tsk("mtcars")
ids = partition(task)
learner$train(task, row_ids = ids$train)
learner$predict(task, row_ids = ids$test)

Regression MultilayerPerceptron Learner

Description

Regressor that uses backpropagation to learn a multi-layer perceptron. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
G removed:
- GUI will be opened
Reason for change: The parameter is removed because we don't want to launch GUI.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.multilayer_perceptron")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
L	numeric	0.3		$[0, 1]$
M	numeric	0.2		$[0, 1]$
N	integer	500		$[1, \infty)$
V	numeric	0		$[0, 100]$
S	integer	0		$[0, \infty)$
E	integer	20		$[1, \infty)$
A	logical	FALSE	TRUE, FALSE	-
B	logical	FALSE	TRUE, FALSE	-
H	untyped	"a"		-
C	logical	FALSE	TRUE, FALSE	-
I	logical	FALSE	TRUE, FALSE	-
R	logical	FALSE	TRUE, FALSE	-
D	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrMultilayerPerceptron

Methods

Public methods

LearnerRegrMultilayerPerceptron$new()
LearnerRegrMultilayerPerceptron$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrMultilayerPerceptron$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrMultilayerPerceptron$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("regr.multilayer_perceptron")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.multilayer_perceptron")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Priority Lasso Learner

Description

Patient outcome prediction based on multi-omics data taking practitioners’ preferences into account. Calls prioritylasso::prioritylasso() from prioritylasso.

Initial parameter values

family is set to "gaussian" and cannot be changed
type.measure set to "mse" (cross-validation measure)

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.priority_lasso")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, prioritylasso

Parameters

Id	Type	Default	Levels	Range
blocks	untyped	-		-
max.coef	untyped	NULL		-
block1.penalization	logical	TRUE	TRUE, FALSE	-
lambda.type	character	lambda.min	lambda.min, lambda.1se	-
standardize	logical	TRUE	TRUE, FALSE	-
nfolds	integer	5		$[1, \infty)$
foldid	untyped	NULL		-
cvoffset	logical	FALSE	TRUE, FALSE	-
cvoffsetnfolds	integer	10		$[1, \infty)$
handle.missingtestdata	character	-	none, omit.prediction, set.zero, impute.block	-
include.allintercepts	logical	FALSE	TRUE, FALSE	-
use.blocks	untyped	"all"		-
alignment	character	lambda	lambda, fraction	-
alpha	numeric	1		$[0, 1]$
big	numeric	9.9e+35		$(-\infty, \infty)$
devmax	numeric	0.999		$[0, 1]$
dfmax	integer	-		$[0, \infty)$
eps	numeric	1e-06		$[0, 1]$
epsnr	numeric	1e-08		$[0, 1]$
exclude	untyped	-		-
exmx	numeric	250		$(-\infty, \infty)$
fdev	numeric	1e-05		$[0, 1]$
gamma	untyped	-		-
grouped	logical	TRUE	TRUE, FALSE	-
intercept	logical	TRUE	TRUE, FALSE	-
keep	logical	FALSE	TRUE, FALSE	-
lambda	untyped	-		-
lambda.min.ratio	numeric	-		$[0, 1]$
lower.limits	untyped	-Inf		-
maxit	integer	100000		$[1, \infty)$
mnlam	integer	5		$[1, \infty)$
mxit	integer	100		$[1, \infty)$
mxitnr	integer	25		$[1, \infty)$
nlambda	integer	100		$[1, \infty)$
offset	untyped	NULL		-
parallel	logical	FALSE	TRUE, FALSE	-
penalty.factor	untyped	-		-
pmax	integer	-		$[0, \infty)$
pmin	numeric	1e-09		$[0, 1]$
prec	numeric	1e-10		$(-\infty, \infty)$
standardize.response	logical	FALSE	TRUE, FALSE	-
thresh	numeric	1e-07		$[0, \infty)$
trace.it	integer	0		$[0, 1]$
type.gaussian	character	-	covariance, naive	-
type.logistic	character	Newton	Newton, modified.Newton	-
type.multinomial	character	ungrouped	ungrouped, grouped	-
upper.limits	untyped	Inf		-
scale.y	logical	FALSE	TRUE, FALSE	-
return.x	logical	TRUE	TRUE, FALSE	-
predict.gamma	numeric	gamma.1se		$(-\infty, \infty)$
relax	logical	FALSE	TRUE, FALSE	-
s	numeric	lambda.1se		$[0, 1]$

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrPriorityLasso

Methods

Public methods

LearnerRegrPriorityLasso$new()
LearnerRegrPriorityLasso$selected_features()
LearnerRegrPriorityLasso$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrPriorityLasso$new()

Method `selected_features()`

Selected features when coef is positive

Usage

LearnerRegrPriorityLasso$selected_features()

Returns

character().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrPriorityLasso$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

HarutyunyanLiana

References

Examples


# Define the Learner and set parameter values
learner = lrn("regr.priority_lasso",
  blocks = list(bp1 = 1:4, bp2 = 5:9, bp3 = 10:28, bp4 = 29:1028))
print(learner)

# Define a Task
task = mlr3::as_task_regr(prioritylasso::pl_data, target = "pl_out")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

 # Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner and set parameter values
learner = lrn("regr.priority_lasso",
  blocks = list(bp1 = 1:4, bp2 = 5:9, bp3 = 10:28, bp4 = 29:1028))
print(learner)

# Define a Task
task = mlr3::as_task_regr(prioritylasso::pl_data, target = "pl_out")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

 # Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Random Forest Learner from Weka

Description

Class for constructing a forest of random trees. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
store_out_of_bag_predictions:
- original id: store-out-of-bag-predictions
output_out_of_bag_complexity_statistics:
- original id: output-out-of-bag-complexity-statistics
num_slots:
- original id: num-slots
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern
attribute-importance removed:
- Compute and output attribute importance (mean impurity decrease method)
Reason for change: The parameter is removed because it's unclear how to actually use it.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.random_forest_weka")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
P	numeric	100		$[0, 100]$
O	logical	FALSE	TRUE, FALSE	-
store_out_of_bag_predictions	logical	FALSE	TRUE, FALSE	-
output_out_of_bag_complexity_statistics	logical	FALSE	TRUE, FALSE	-
print	logical	FALSE	TRUE, FALSE	-
I	integer	100		$[1, \infty)$
num_slots	integer	1		$(-\infty, \infty)$
K	integer	0		$(-\infty, \infty)$
M	integer	1		$[1, \infty)$
V	numeric	0.001		$(-\infty, \infty)$
S	integer	1		$(-\infty, \infty)$
depth	integer	0		$[0, \infty)$
N	integer	0		$(-\infty, \infty)$
U	logical	FALSE	TRUE, FALSE	-
B	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomForestWeka

Methods

Public methods

LearnerRegrRandomForestWeka$new()
LearnerRegrRandomForestWeka$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrRandomForestWeka$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomForestWeka$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

Examples


# Define the Learner
learner = mlr3::lrn("regr.random_forest_weka")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.random_forest_weka")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Random Tree Learner

Description

Tree that considers K randomly chosen attributes at each node. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.random_tree")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
K	integer	0		$[0, \infty)$
M	integer	1		$[1, \infty)$
V	numeric	0.001		$(-\infty, \infty)$
S	integer	1		$(-\infty, \infty)$
depth	integer	0		$[0, \infty)$
N	integer	0		$[0, \infty)$
U	logical	FALSE	TRUE, FALSE	-
B	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomTree

Methods

Public methods

LearnerRegrRandomTree$new()
LearnerRegrRandomTree$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrRandomTree$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomTree$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("regr.random_tree")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.random_tree")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Random Forest Learner

Description

Random forest for regression. Calls randomForest::randomForest() from randomForest.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.randomForest")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, randomForest

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		$[1, \infty)$
mtry	integer	-		$[1, \infty)$
replace	logical	TRUE	TRUE, FALSE	-
strata	untyped	-		-
sampsize	untyped	-		-
nodesize	integer	5		$[1, \infty)$
maxnodes	integer	-		$[1, \infty)$
importance	character	FALSE	mse, nudepurity, none	-
localImp	logical	FALSE	TRUE, FALSE	-
proximity	logical	FALSE	TRUE, FALSE	-
oob.prox	logical	-	TRUE, FALSE	-
norm.votes	logical	TRUE	TRUE, FALSE	-
do.trace	logical	FALSE	TRUE, FALSE	-
keep.forest	logical	TRUE	TRUE, FALSE	-
keep.inbag	logical	FALSE	TRUE, FALSE	-
predict.all	logical	FALSE	TRUE, FALSE	-
nodes	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomForest

Methods

Public methods

LearnerRegrRandomForest$new()
LearnerRegrRandomForest$importance()
LearnerRegrRandomForest$oob_error()
LearnerRegrRandomForest$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrRandomForest$new()

Method `importance()`

The importance scores are extracted from the slot importance. Parameter 'importance' must be set to either "mse" or "nodepurity".

Usage

LearnerRegrRandomForest$importance()

Returns

Named numeric().

Method `oob_error()`

OOB errors are extracted from the model slot mse.

Usage

LearnerRegrRandomForest$oob_error()

Returns

numeric(1).

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomForest$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

pat-s

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

Examples


# Define the Learner
learner = mlr3::lrn("regr.randomForest", importance = "mse")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.randomForest", importance = "mse")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Decision Tree Learner

Description

Fast decision tree learner. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.reptree")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
M	integer	2		$(-\infty, \infty)$
V	numeric	0.001		$(-\infty, \infty)$
N	integer	3		$(-\infty, \infty)$
S	integer	1		$(-\infty, \infty)$
P	logical	-	TRUE, FALSE	-
L	integer	-1		$(-\infty, \infty)$
I	integer	0		$(-\infty, \infty)$
R	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrREPTree

Methods

Public methods

LearnerRegrREPTree$new()
LearnerRegrREPTree$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrREPTree$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrREPTree$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("regr.reptree")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.reptree")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Random Forest SRC Learner

Description

Random forest for regression. Calls randomForestSRC::rfsrc() from randomForestSRC.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.rfsrc")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, randomForestSRC

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		$[1, \infty)$
mtry	integer	-		$[1, \infty)$
mtry.ratio	numeric	-		$[0, 1]$
nodesize	integer	15		$[1, \infty)$
nodedepth	integer	-		$[1, \infty)$
splitrule	character	mse	mse, quantile.regr, la.quantile.regr	-
nsplit	integer	10		$[0, \infty)$
importance	character	FALSE	FALSE, TRUE, none, permute, random, anti	-
block.size	integer	10		$[1, \infty)$
bootstrap	character	by.root	by.root, by.node, none, by.user	-
samptype	character	swor	swor, swr	-
samp	untyped	-		-
membership	logical	FALSE	TRUE, FALSE	-
sampsize	untyped	-		-
sampsize.ratio	numeric	-		$[0, 1]$
na.action	character	na.omit	na.omit, na.impute	-
nimpute	integer	1		$[1, \infty)$
ntime	integer	-		$[1, \infty)$
cause	integer	-		$[1, \infty)$
proximity	character	FALSE	FALSE, TRUE, inbag, oob, all	-
distance	character	FALSE	FALSE, TRUE, inbag, oob, all	-
forest.wt	character	FALSE	FALSE, TRUE, inbag, oob, all	-
xvar.wt	untyped	-		-
split.wt	untyped	-		-
forest	logical	TRUE	TRUE, FALSE	-
var.used	character	FALSE	FALSE, all.trees, by.tree	-
split.depth	character	FALSE	FALSE, all.trees, by.tree	-
seed	integer	-		$(-\infty, -1]$
do.trace	logical	FALSE	TRUE, FALSE	-
statistics	logical	FALSE	TRUE, FALSE	-
get.tree	untyped	-		-
outcome	character	train	train, test	-
ptn.count	integer	0		$[0, \infty)$
cores	integer	1		$[1, \infty)$
save.memory	logical	FALSE	TRUE, FALSE	-
perf.type	character	-	none	-
case.depth	logical	FALSE	TRUE, FALSE	-

Custom mlr3 parameters

mtry: This hyperparameter can alternatively be set via the added hyperparameter mtry.ratio as mtry = max(ceiling(mtry.ratio * n_features), 1). Note that mtry and mtry.ratio are mutually exclusive.
sampsize: This hyperparameter can alternatively be set via the added hyperparameter sampsize.ratio as sampsize = max(ceiling(sampsize.ratio * n_obs), 1). Note that sampsize and sampsize.ratio are mutually exclusive.
cores: This value is set as the option rf.cores during training and is set to 1 by default.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomForestSRC

Methods

Public methods

LearnerRegrRandomForestSRC$new()
LearnerRegrRandomForestSRC$importance()
LearnerRegrRandomForestSRC$selected_features()
LearnerRegrRandomForestSRC$oob_error()
LearnerRegrRandomForestSRC$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrRandomForestSRC$new()

Method `importance()`

The importance scores are extracted from the model slot importance.

Usage

LearnerRegrRandomForestSRC$importance()

Returns

Named numeric().

Method `selected_features()`

Selected features are extracted from the model slot var.used.

Usage

LearnerRegrRandomForestSRC$selected_features()

Returns

character().

Method `oob_error()`

OOB error extracted from the model slot err.rate.

Usage

LearnerRegrRandomForestSRC$oob_error()

Returns

numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomForestSRC$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

Examples


# Define the Learner
learner = mlr3::lrn("regr.rfsrc", importance = "TRUE")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.rfsrc", importance = "TRUE")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")
# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Random Planted Forest Learner

Description

Random Planted Forest: A directly interpretable tree ensemble.

Calls randomPlantedForest::rpf() from 'randomPlantedForest'.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.rpf")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, randomPlantedForest

Parameters

Id	Type	Default	Levels	Range
max_interaction	integer	1		$[0, \infty)$
max_interaction_ratio	numeric	-		$[0, 1]$
max_interaction_limit	integer	-		$[1, \infty)$
ntrees	integer	50		$[1, \infty)$
splits	integer	30		$[1, \infty)$
split_try	integer	10		$[1, \infty)$
t_try	numeric	0.4		$[0, 1]$
deterministic	logical	FALSE	TRUE, FALSE	-
nthreads	integer	1		$[1, \infty)$
cv	logical	FALSE	TRUE, FALSE	-
purify	logical	FALSE	TRUE, FALSE	-

Custom mlr3 parameters

max_interaction:
- This hyperparameter can alternatively be set via max_interaction_ratio as max_interaction = max(ceiling(max_interaction_ratio * n_features), 1). The parameter max_interaction_limit can optionally be set as an upper bound, such that max_interaction_ratio * min(n_features, max_interaction_limit) is used instead. This is analogous to mtry.ratio in classif.ranger, with max_interaction_limit as an additional constraint. The parameter max_interaction_limit is initialized to Inf.

Installation

Package 'randomPlantedForest' is not on CRAN and has to be installed from GitHub via remotes::install_github("PlantedML/randomPlantedForest").

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRandomPlantedForest

Methods

Public methods

LearnerRegrRandomPlantedForest$new()
LearnerRegrRandomPlantedForest$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrRandomPlantedForest$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrRandomPlantedForest$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

jemus42

References

Hiabu, Munir, Mammen, Enno, Meyer, T. J (2023). “Random Planted Forest: a directly interpretable tree ensemble.” arXiv preprint arXiv:2012.14563. doi:10.48550/ARXIV.2012.14563.

Examples


# Define the Learner
learner = mlr3::lrn("regr.rpf")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.rpf")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Response Surface Model Learner

Description

Fit a linear model with a response-surface component. Calls rsm::rsm() from rsm.

Custom mlr3 parameters

modelfun: This parameter controls how the formula for rsm::rsm() is created. Possible values are:
- "FO" - first order
- "TWI" - wo-way interactions, this is with 1st oder terms
- "SO" - full second order

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.rsm")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, rsm

Parameters

Id	Type	Default	Levels
modelfun	character	-	FO, TWI, SO

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRSM

Methods

Public methods

LearnerRegrRSM$new()
LearnerRegrRSM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrRSM$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrRSM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

sebffischer

References

Lenth, V R (2010). “Response-surface methods in R, using rsm.” Journal of Statistical Software, 32, 1–17.

Examples


# Define the Learner
learner = mlr3::lrn("regr.rsm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.rsm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Relevance Vector Machine Learner

Description

Bayesian version of the support vector machine. Parameters sigma, degree, scale, offset, order, length, lambda, and normalized are added to make tuning kpar easier. If kpar is provided then these new parameters are ignored. If none are provided then the default "automatic" is used for kpar. Calls kernlab::rvm() from package kernlab.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.rvm")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3extralearners, kernlab

Parameters

Id	Type	Default	Levels	Range
kernel	character	rbfdot	rbfdot, polydot, vanilladot, tanhdot, laplacedot, besseldot, anovadot, splinedot, stringdot	-
sigma	numeric	-		$(-\infty, \infty)$
degree	numeric	-		$(-\infty, \infty)$
scale	numeric	-		$(-\infty, \infty)$
offset	numeric	-		$(-\infty, \infty)$
order	numeric	-		$(-\infty, \infty)$
length	integer	-		$[0, \infty)$
lambda	numeric	-		$(-\infty, \infty)$
normalized	logical	-	TRUE, FALSE	-
kpar	untyped	"automatic"		-
alpha	untyped	5		-
var	numeric	0.1		$[0.001, \infty)$
var.fix	logical	FALSE	TRUE, FALSE	-
iterations	integer	100		$[0, \infty)$
tol	numeric	2.220446e-16		$[0, \infty)$
minmaxdiff	numeric	0.001		$[0, \infty)$
verbosity	logical	FALSE	TRUE, FALSE	-
fit	logical	TRUE	TRUE, FALSE	-
na.action	untyped	na.omit		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRVM

Methods

Public methods

LearnerRegrRVM$new()
LearnerRegrRVM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrRVM$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrRVM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Karatzoglou, Alexandros, Smola, Alex, Hornik, Kurt, Zeileis, Achim (2004). “kernlab-an S4 package for kernel methods in R.” Journal of statistical software, 11(9), 1–20.

Examples


# Define the Learner
learner = mlr3::lrn("regr.rvm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.rvm")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Stochastic Gradient Descent Learner

Description

Stochastic Gradient Descent for learning various linear models. Calls RWeka::make_Weka_classifier() from RWeka.

Initial parameter values

F:
- Has only 3 out of 5 original loss functions: 2 = squared loss (regression), 3 = epsilon insensitive loss (regression) and 4 = Huber loss (regression) with 2 (squared loss) being the new default
- Reason for change: this learner should only contain loss functions appropriate for regression tasks

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.sgd")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
F	character	2	2, 3, 4	-
L	numeric	0.01		$(-\infty, \infty)$
R	numeric	1e-04		$(-\infty, \infty)$
E	integer	500		$(-\infty, \infty)$
C	numeric	0.001		$(-\infty, \infty)$
N	logical	-	TRUE, FALSE	-
M	logical	-	TRUE, FALSE	-
S	integer	1		$(-\infty, \infty)$
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrSGD

Methods

Public methods

LearnerRegrSGD$new()
LearnerRegrSGD$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrSGD$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrSGD$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("regr.sgd")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.sgd")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression SimpleLinearRegression Learner from Weka

Description

Simple linear regression model that picks the attribute that results in the lowest squared error. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
additional_stats:
- original id: additional-stats
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.simple_linear_regression")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
additional_stats	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrSimpleLinearRegression

Methods

Public methods

LearnerRegrSimpleLinearRegression$new()
LearnerRegrSimpleLinearRegression$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrSimpleLinearRegression$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrSimpleLinearRegression$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

Examples


# Define the Learner
learner = mlr3::lrn("regr.simple_linear_regression")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.simple_linear_regression")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Regression Support Vector Machine Learner

Description

Support Vector Machine for regression. Calls RWeka::make_Weka_classifier() from RWeka.

Custom mlr3 parameters

output_debug_info:
- original id: output-debug-info
do_not_check_capabilities:
- original id: do-not-check-capabilities
num_decimal_places:
- original id: num-decimal-places
batch_size:
- original id: batch-size
T_improved:
- original id: T
V_improved:
- original id: V
P_improved:
- original id: P
L_improved:
- original id: L (duplicated L for when I is set to RegSMOImproved)
W_improved:
- original id: W
C_poly:
- original id: C
E_poly:
- original id: E
L_poly:
- original id: L (duplicated L for when K is set to PolyKernel)
Reason for change: This learner contains changed ids of the following control arguments since their ids contain irregular pattern

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.smo_reg")

Meta Information

Task type: “regr”
Predict Types: “response”
Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, RWeka

Parameters

Id	Type	Default	Levels	Range
subset	untyped	-		-
na.action	untyped	-		-
C	numeric	1		$(-\infty, \infty)$
N	character	0	0, 1, 2	-
I	character	RegSMOImproved	RegSMO, RegSMOImproved	-
K	character	PolyKernel	NormalizedPolyKernel, PolyKernel, Puk, RBFKernel, StringKernel	-
T_improved	numeric	0.001		$(-\infty, \infty)$
V_improved	logical	TRUE	TRUE, FALSE	-
P_improved	numeric	1e-12		$(-\infty, \infty)$
L_improved	numeric	0.001		$(-\infty, \infty)$
W_improved	integer	1		$(-\infty, \infty)$
C_poly	integer	250007		$(-\infty, \infty)$
E_poly	numeric	1		$(-\infty, \infty)$
L_poly	logical	FALSE	TRUE, FALSE	-
output_debug_info	logical	FALSE	TRUE, FALSE	-
do_not_check_capabilities	logical	FALSE	TRUE, FALSE	-
num_decimal_places	integer	2		$[1, \infty)$
batch_size	integer	100		$[1, \infty)$
options	untyped	NULL		-

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrSMOreg

Methods

Public methods

LearnerRegrSMOreg$new()
LearnerRegrSMOreg$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerRegrSMOreg$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerRegrSMOreg$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

damirpolat

References

Shevade S, Keerthi S, Bhattacharyya C, Murthy K (1999). “Improvements to the SMO Algorithm for SVM Regression.” In IEEE Transactions on Neural Networks.

Examples


# Define the Learner
learner = mlr3::lrn("regr.smo_reg")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("regr.smo_reg")
print(learner)

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Akritas Estimator Learner

Description

Survival akritas estimator. Calls survivalmodels::akritas() from package 'survivalmodels'.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.akritas() function. The survival matrix uses the unique time points from the training set. We advise to set the parameter ntime which allows to adjust the granularity of these time points to a reasonable number (e.g. 150). This avoids large execution times during prediction.
crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.akritas")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6

Parameters

Id	Type	Default	Levels	Range
lambda	numeric	0.5		$[0, 1]$
reverse	logical	FALSE	TRUE, FALSE	-
ntime	integer	NULL		$[1, \infty)$
round_time	integer	2		$[0, \infty)$

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvAkritas

Methods

Public methods

LearnerSurvAkritas$new()
LearnerSurvAkritas$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvAkritas$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvAkritas$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Akritas, G M (1994). “Nearest neighbor estimation of a bivariate distribution under random censoring.” The Annals of Statistics, 1299–1327.

Examples


# Define the Learner
learner = mlr3::lrn("surv.akritas")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.akritas")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Accelerated Oblique Random Survival Forest Learner

Description

Accelerated oblique random survival forest. Calls aorsf::orsf() from aorsf. Note that although the learner has the property "missing" and it can in principle deal with missing values, the behaviour has to be configured using the parameter na_action.

Initial parameter values

n_thread: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.

mtry:
- This hyperparameter can alternatively be set via the added hyperparameter mtry_ratio as mtry = max(ceiling(mtry_ratio * n_features), 1). Note that mtry and mtry_ratio are mutually exclusive.

Prediction types

This learner returns three prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and (unique event) time points in columns. Calculated using the internal predict.ObliqueForest() function.
response: the restricted mean survival time of each test observation, derived from the survival matrix prediction (distr).
crank: the expected mortality using mlr3proba::.surv_return().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.aorsf")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “response”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3proba, mlr3extralearners, aorsf, pracma

Parameters

Id	Type	Default	Levels	Range
n_tree	integer	500		$[1, \infty)$
n_split	integer	5		$[1, \infty)$
n_retry	integer	3		$[0, \infty)$
n_thread	integer	0		$[0, \infty)$
pred_aggregate	logical	TRUE	TRUE, FALSE	-
pred_simplify	logical	FALSE	TRUE, FALSE	-
oobag	logical	FALSE	TRUE, FALSE	-
mtry	integer	NULL		$[1, \infty)$
mtry_ratio	numeric	-		$[0, 1]$
sample_with_replacement	logical	TRUE	TRUE, FALSE	-
sample_fraction	numeric	0.632		$[0, 1]$
control_type	character	fast	fast, cph, net	-
split_rule	character	logrank	logrank, cstat	-
control_fast_do_scale	logical	FALSE	TRUE, FALSE	-
control_fast_ties	character	efron	efron, breslow	-
control_cph_ties	character	efron	efron, breslow	-
control_cph_eps	numeric	1e-09		$[0, \infty)$
control_cph_iter_max	integer	20		$[1, \infty)$
control_net_alpha	numeric	0.5		$(-\infty, \infty)$
control_net_df_target	integer	NULL		$[1, \infty)$
leaf_min_events	integer	1		$[1, \infty)$
leaf_min_obs	integer	5		$[1, \infty)$
split_min_events	integer	5		$[1, \infty)$
split_min_obs	integer	10		$[1, \infty)$
split_min_stat	numeric	NULL		$[0, \infty)$
oobag_pred_type	character	risk	none, surv, risk, chf, mort	-
importance	character	anova	none, anova, negate, permute	-
importance_max_pvalue	numeric	0.01		$[1e-04, 0.9999]$
tree_seeds	integer	NULL		$[1, \infty)$
oobag_pred_horizon	numeric	NULL		$[0, \infty)$
oobag_eval_every	integer	NULL		$[1, \infty)$
oobag_fun	untyped	NULL		-
attach_data	logical	TRUE	TRUE, FALSE	-
verbose_progress	logical	FALSE	TRUE, FALSE	-
na_action	character	fail	fail, omit, impute_meanmode	-

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvAorsf

Methods

Public methods

LearnerSurvAorsf$new()
LearnerSurvAorsf$oob_error()
LearnerSurvAorsf$importance()
LearnerSurvAorsf$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvAorsf$new()

Method `oob_error()`

OOB concordance error extracted from the model slot eval_oobag$stat_values

Usage

LearnerSurvAorsf$oob_error()

Returns

numeric().

Method `importance()`

The importance scores are extracted from the model.

Usage

LearnerSurvAorsf$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvAorsf$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

bcjaeger

References

Jaeger BC, Long DL, Long DM, Sims M, Szychowski JM, Min Y, Mcclure LA, Howard G, Simon N (2019). “Oblique random survival forests.” The Annals of Applied Statistics, 13(3). doi:10.1214/19-aoas1261.

Jaeger BC, Welden S, Lenoir K, Speiser JL, Segar MW, Pandey A, Pajewski NM (2023). “Accelerated and interpretable oblique random survival forests.” Journal of Computational and Graphical Statistics, 1–16. doi:10.1080/10618600.2023.2231048.

Examples


# Define the Learner
learner = mlr3::lrn("surv.aorsf")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.aorsf")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Bayesian Additive Regression Trees Learner

Description

Fits a Bayesian Additive Regression Trees (BART) learner to right-censored survival data. Calls BART::mc.surv.bart() from BART.

Prediction types

This learner returns two prediction types:

distr: a 3d survival array with observations as 1st dimension, time points as 2nd and the posterior draws as 3rd dimension. Calculated using the internal predict.survbart() function.
crank: the expected mortality using mlr3proba::.surv_return(). The parameter which.curve decides which posterior draw (3rd dimension) will be used for the calculation of the expected mortality. Note that the median posterior is by default used for the calculation of survival measures that require a distr prediction, see more info on PredictionSurv.

Initial parameter values

mc.cores is initialized to 1 to avoid threading conflicts with future.

Custom mlr3 parameters

quiet allows to suppress messages generated by the wrapped C++ code. Is initialized to TRUE.
importance allows to choose the type of importance. Default is count, see documentation of method ⁠$importance()⁠ for more details.
which.curve allows to choose which posterior draw will be used for the calculation of the crank prediction. If between (0,1) it is taken as the quantile of the curves otherwise if greater than 1 it is taken as the curve index, can also be 'mean'. By default the median posterior is used, i.e. which.curve is 0.5.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.bart")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3proba, BART

Parameters

Id	Type	Default	Levels	Range
K	numeric	NULL		$[1, \infty)$
events	untyped	NULL		-
ztimes	untyped	NULL		-
zdelta	untyped	NULL		-
sparse	logical	FALSE	TRUE, FALSE	-
theta	numeric	0		$(-\infty, \infty)$
omega	numeric	1		$(-\infty, \infty)$
a	numeric	0.5		$[0.5, 1]$
b	numeric	1		$(-\infty, \infty)$
augment	logical	FALSE	TRUE, FALSE	-
rho	numeric	NULL		$(-\infty, \infty)$
usequants	logical	FALSE	TRUE, FALSE	-
rm.const	logical	TRUE	TRUE, FALSE	-
type	character	pbart	pbart, lbart	-
ntype	integer	-		$[1, 3]$
k	numeric	2		$[0, \infty)$
power	numeric	2		$[0, \infty)$
base	numeric	0.95		$[0, 1]$
offset	numeric	NULL		$(-\infty, \infty)$
ntree	integer	50		$[1, \infty)$
numcut	integer	100		$[1, \infty)$
ndpost	integer	1000		$[1, \infty)$
nskip	integer	250		$[0, \infty)$
keepevery	integer	10		$[1, \infty)$
printevery	integer	100		$[1, \infty)$
seed	integer	99		$(-\infty, \infty)$
mc.cores	integer	2		$[1, \infty)$
nice	integer	19		$[0, 19]$
openmp	logical	TRUE	TRUE, FALSE	-
quiet	logical	TRUE	TRUE, FALSE	-
importance	character	count	count, prob	-
which.curve	numeric	-		$[0, \infty)$

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvLearnerSurvBART

Methods

Public methods

LearnerSurvLearnerSurvBART$new()
LearnerSurvLearnerSurvBART$importance()
LearnerSurvLearnerSurvBART$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvLearnerSurvBART$new()

Method `importance()`

Two types of importance scores are supported based on the value of the parameter importance:

prob: The mean selection probability of each feature in the trees, extracted from the slot varprob.mean. If sparse = FALSE (default), this is a fixed constant. Recommended to use this option when sparse = TRUE.
count: The mean observed count of each feature in the trees (average number of times the feature was used in a tree decision rule across all posterior draws), extracted from the slot varcount.mean. This is the default importance scores.

In both cases, higher values signify more important variables.

Usage

LearnerSurvLearnerSurvBART$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvLearnerSurvBART$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

bblodfon

References

Chipman, A H, George, I E, McCulloch, E R (2010). “BART: Bayesian additive regression trees.” The Annals of Applied Statistics, 4(1), 266–298.

Examples

lrn("surv.bart")
lrn("surv.bart")

Gradient Boosting with Regression Trees Survival Learner

Description

Gradient boosting with regression trees for survival analysis. Calls mboost::blackboost() from mboost.

Details

distr prediction made by mboost::survFit().

Prediction types

This learner returns two to three prediction types:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using mboost::predict.blackboost(). If the family parameter is not "coxph", -lp is returned, since non-coxph families represent AFT-style distributions where lower lp values indicate higher risk.
crank: same as lp.
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using mboost::survFit(). This prediction type is present only when the family distribution parameter is equal to "coxph" (default). By default the Breslow estimator is used for computing the baseline hazard.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.blackboost")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma

Parameters

Id	Type	Default	Levels	Range
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
offset	untyped	-		-
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		$[0, \infty)$
nu	numeric	0.1		$[0, 1]$
risk	character	-	inbag, oobag, none	-
stopintern	logical	FALSE	TRUE, FALSE	-
trace	logical	FALSE	TRUE, FALSE	-
oobweights	untyped	-		-
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Bonferroni	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
maxpts	integer	25000		$[1, \infty)$
abseps	numeric	0.001		$(-\infty, \infty)$
releps	numeric	0		$(-\infty, \infty)$
nmax	untyped	-		-
alpha	numeric	0.05		$[0, 1]$
mincriterion	numeric	0.95		$[0, 1]$
logmincriterion	numeric	-0.05129329		$(-\infty, 0]$
minsplit	integer	20		$[0, \infty)$
minbucket	integer	7		$[0, \infty)$
minprob	numeric	0.01		$[0, 1]$
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
nresample	integer	9999		$[1, \infty)$
tol	numeric	1.490116e-08		$[0, \infty)$
maxsurrogate	integer	0		$[0, \infty)$
mtry	integer	-		$[0, \infty)$
maxdepth	integer	-		$[0, \infty)$
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		$[1, \infty)$
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
sigma	numeric	0.1		$[0, 1]$
ipcw	untyped	1		-
na.action	untyped	stats::na.omit		-

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvBlackBoost

Methods

Public methods

LearnerSurvBlackBoost$new()
LearnerSurvBlackBoost$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvBlackBoost$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvBlackBoost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

Examples


# Define the Learner
learner = mlr3::lrn("surv.blackboost")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.blackboost")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Conditional Random Forest Learner

Description

A random forest based on conditional inference trees (ctree). Calls partykit::cforest() from partykit.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal partykit::predict.cforest() function.
crank: the expected mortality using mlr3proba::.surv_return().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.cforest")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3proba, mlr3extralearners, partykit, sandwich, coin

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		$[1, \infty)$
replace	logical	FALSE	TRUE, FALSE	-
fraction	numeric	0.632		$[0, 1]$
mtry	integer	-		$[0, \infty)$
mtryratio	numeric	-		$[0, 1]$
applyfun	untyped	-		-
cores	integer	NULL		$(-\infty, \infty)$
trace	logical	FALSE	TRUE, FALSE	-
offset	untyped	-		-
cluster	untyped	-		-
na.action	untyped	"stats::na.pass"		-
scores	untyped	-		-
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Univariate	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
alpha	numeric	0.05		$[0, 1]$
mincriterion	numeric	0.95		$[0, 1]$
logmincriterion	numeric	-0.05129329		$(-\infty, \infty)$
minsplit	integer	20		$[1, \infty)$
minbucket	integer	7		$[1, \infty)$
minprob	numeric	0.01		$[0, 1]$
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
nresample	integer	9999		$[1, \infty)$
tol	numeric	1.490116e-08		$[0, \infty)$
maxsurrogate	integer	0		$[0, \infty)$
numsurrogate	logical	FALSE	TRUE, FALSE	-
maxdepth	integer	Inf		$[0, \infty)$
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		$[0, \infty)$
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	TRUE	TRUE, FALSE	-
saveinfo	logical	FALSE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
maxvar	integer	-		$[1, \infty)$
OOB	logical	FALSE	TRUE, FALSE	-
simplify	logical	TRUE	TRUE, FALSE	-
scale	logical	TRUE	TRUE, FALSE	-
maxpts	integer	25000		$(-\infty, \infty)$
abseps	numeric	0.001		$[0, \infty)$
releps	numeric	0		$[0, \infty)$

Initial parameter values

cores: This parameter is initialized to 1 (default is NULL) to avoid threading conflicts with future.

Custom mlr3 parameters

mtry:
- This hyperparameter can alternatively be set via the added hyperparameter mtryratio as mtry = max(ceiling(mtryratio * n_features), 1). Note that mtry and mtryratio are mutually exclusive.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCForest

Methods

Public methods

LearnerSurvCForest$new()
LearnerSurvCForest$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvCForest$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvCForest$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Examples


task = tsk("rats")
learner = lrn("surv.cforest", ntree = 50)
splits = partition(task)
learner$train(task, splits$train)
pred = learner$predict(task, splits$test)

task = tsk("rats")
learner = lrn("surv.cforest", ntree = 50)
splits = partition(task)
learner$train(task, splits$train)
pred = learner$predict(task, splits$test)

Survival Cox Model with Likelihood Based Boosting Learner

Description

Fit a Survival Cox model with a likelihood based boosting algorithm. Calls CoxBoost::CoxBoost() from package 'CoxBoost'.

Details

Use LearnerSurvCoxboost and LearnerSurvCVCoxboost for Cox boosting without and with internal cross-validation of boosting step number, respectively. Tuning using the internal optimizer in LearnerSurvCVCoxboost may be more efficient when tuning stepno only. However, for tuning multiple hyperparameters, mlr3tuning and LearnerSurvCoxboost will likely give better results.

Prediction types

This learner returns three prediction types, using the internal predict.CoxBoost() function:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation.
crank: same as lp.
distr: a 2d survival matrix, with observations as rows and time points as columns. The internal transformation uses the Breslow estimator to compute the baseline hazard and compose the survival distributions from the lp predictions.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.coxboost")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, CoxBoost, pracma

Parameters

Id	Type	Default	Levels	Range
unpen.index	untyped	-		-
standardize	logical	TRUE	TRUE, FALSE	-
stepno	integer	100		$[0, \infty)$
penalty	numeric	-		$(-\infty, \infty)$
criterion	character	pscore	pscore, score, hpscore, hscore	-
stepsize.factor	numeric	1		$(-\infty, \infty)$
sf.scheme	character	sigmoid	sigmoid, linear	-
pendistmat	untyped	-		-
connected.index	untyped	-		-
x.is.01	logical	FALSE	TRUE, FALSE	-
return.score	logical	TRUE	TRUE, FALSE	-
trace	logical	FALSE	TRUE, FALSE	-
at.step	untyped	-		-

Installation

The package 'CoxBoost' is not on CRAN and has to be installed from GitHub using remotes::install_github("binderh/CoxBoost").

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCoxboost

Methods

Public methods

LearnerSurvCoxboost$new()
LearnerSurvCoxboost$selected_features()
LearnerSurvCoxboost$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvCoxboost$new()

Method `selected_features()`

Returns the set of selected features which have non-zero coefficients. Calls the internal coef.CoxBoost() function.

Usage

LearnerSurvCoxboost$selected_features(at_step = NULL)

Arguments

at_step: (integer(1))
Which boosting step to get the coefficients for. If no step is given (default), the final boosting step is used.

Returns

(character()) vector of feature names.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvCoxboost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Binder, Harald, Allignol, Arthur, Schumacher, Martin, Beyersmann, Jan (2009). “Boosting for high-dimensional time-to-event data with competing risks.” Bioinformatics, 25(7), 890–896.

Examples


# Define the Learner
learner = mlr3::lrn("surv.coxboost")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.coxboost")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Cox-Time Learner

Description

Cox-Time survival model. Calls survivalmodels::coxtime() from package 'survivalmodels'.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.pycox() function.
crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.coxtime")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6, reticulate

Parameters

Id	Type	Default	Levels	Range
frac	numeric	0		$[0, 1]$
standardize_time	logical	FALSE	TRUE, FALSE	-
log_duration	logical	FALSE	TRUE, FALSE	-
with_mean	logical	TRUE	TRUE, FALSE	-
with_std	logical	TRUE	TRUE, FALSE	-
num_nodes	untyped	c(32L, 32L)		-
batch_norm	logical	TRUE	TRUE, FALSE	-
dropout	numeric	-		$[0, 1]$
activation	character	relu	celu, elu, gelu, glu, hardshrink, hardsigmoid, hardswish, hardtanh, relu6, leakyrelu, ...	-
device	untyped	-		-
shrink	numeric	0		$[0, \infty)$
optimizer	character	adam	adadelta, adagrad, adam, adamax, adamw, asgd, rmsprop, rprop, sgd, sparse_adam	-
rho	numeric	0.9		$(-\infty, \infty)$
eps	numeric	1e-08		$(-\infty, \infty)$
lr	numeric	1		$(-\infty, \infty)$
weight_decay	numeric	0		$(-\infty, \infty)$
learning_rate	numeric	0.01		$(-\infty, \infty)$
lr_decay	numeric	0		$(-\infty, \infty)$
betas	untyped	c(0.9, 0.999)		-
amsgrad	logical	FALSE	TRUE, FALSE	-
lambd	numeric	1e-04		$[0, \infty)$
alpha	numeric	0.75		$[0, \infty)$
t0	numeric	1e+06		$(-\infty, \infty)$
momentum	numeric	0		$(-\infty, \infty)$
centered	logical	TRUE	TRUE, FALSE	-
etas	untyped	c(0.5, 1.2)		-
step_sizes	untyped	c(1e-06, 50)		-
dampening	numeric	0		$(-\infty, \infty)$
nesterov	logical	FALSE	TRUE, FALSE	-
batch_size	integer	256		$(-\infty, \infty)$
epochs	integer	1		$[1, \infty)$
verbose	logical	TRUE	TRUE, FALSE	-
num_workers	integer	0		$(-\infty, \infty)$
shuffle	logical	TRUE	TRUE, FALSE	-
best_weights	logical	FALSE	TRUE, FALSE	-
early_stopping	logical	FALSE	TRUE, FALSE	-
min_delta	numeric	0		$(-\infty, \infty)$
patience	integer	10		$(-\infty, \infty)$

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCoxtime

Methods

Public methods

LearnerSurvCoxtime$new()
LearnerSurvCoxtime$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvCoxtime$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvCoxtime$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Kvamme, Håvard, Borgan Ø, Scheel I (2019). “Time-to-event prediction with neural networks and Cox regression.” arXiv preprint arXiv:1907.00825.

Examples

lrn("surv.coxtime")
lrn("surv.coxtime")

Survival Conditional Inference Tree Learner

Description

Survival Partition Tree where a significance test is used to determine the univariate splits. Calls partykit::ctree() from partykit.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal partykit::predict.party() function.
crank: the expected mortality using mlr3proba::.surv_return().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.ctree")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3proba, mlr3extralearners, partykit, coin, sandwich

Parameters

Id	Type	Default	Levels	Range
teststat	character	quadratic	quadratic, maximum	-
splitstat	character	quadratic	quadratic, maximum	-
splittest	logical	FALSE	TRUE, FALSE	-
testtype	character	Bonferroni	Bonferroni, MonteCarlo, Univariate, Teststatistic	-
nmax	untyped	-		-
alpha	numeric	0.05		$[0, 1]$
mincriterion	numeric	0.95		$[0, 1]$
logmincriterion	numeric	-		$(-\infty, \infty)$
minsplit	integer	20		$[1, \infty)$
minbucket	integer	7		$[1, \infty)$
minprob	numeric	0.01		$[0, \infty)$
stump	logical	FALSE	TRUE, FALSE	-
lookahead	logical	FALSE	TRUE, FALSE	-
MIA	logical	FALSE	TRUE, FALSE	-
nresample	integer	9999		$[1, \infty)$
tol	numeric	-		$[0, \infty)$
maxsurrogate	integer	0		$[0, \infty)$
numsurrogate	logical	FALSE	TRUE, FALSE	-
mtry	integer	Inf		$[0, \infty)$
maxdepth	integer	Inf		$[0, \infty)$
maxvar	integer	-		$[1, \infty)$
multiway	logical	FALSE	TRUE, FALSE	-
splittry	integer	2		$[0, \infty)$
intersplit	logical	FALSE	TRUE, FALSE	-
majority	logical	FALSE	TRUE, FALSE	-
caseweights	logical	FALSE	TRUE, FALSE	-
applyfun	untyped	-		-
cores	integer	NULL		$(-\infty, \infty)$
saveinfo	logical	TRUE	TRUE, FALSE	-
update	logical	FALSE	TRUE, FALSE	-
splitflavour	character	ctree	ctree, exhaustive	-
offset	untyped	-		-
cluster	untyped	-		-
scores	untyped	-		-
doFit	logical	TRUE	TRUE, FALSE	-
maxpts	integer	25000		$(-\infty, \infty)$
abseps	numeric	0.001		$[0, \infty)$
releps	numeric	0		$[0, \infty)$

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCTree

Methods

Public methods

LearnerSurvCTree$new()
LearnerSurvCTree$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvCTree$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvCTree$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

adibender

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html.

Examples


# Define the Learner
learner = mlr3::lrn("surv.ctree")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.ctree")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Cox Model with Cross-Validation Likelihood Based Boosting Learner

Description

Fits a survival Cox model using likelihood based boosting and internal cross-validation for the number of steps. Calls CoxBoost::CoxBoost() or CoxBoost::cv.CoxBoost() from package 'CoxBoost'.

Details

If penalty == "optimCoxBoostPenalty" then CoxBoost::optimCoxBoostPenalty is used to determine the penalty value to be used in CoxBoost::cv.CoxBoost.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.cv_coxboost")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, CoxBoost, pracma

Parameters

Id	Type	Default	Levels	Range
maxstepno	integer	100		$[0, \infty)$
K	integer	10		$[2, \infty)$
type	character	verweij	verweij, naive	-
folds	untyped	NULL		-
minstepno	integer	50		$[0, \infty)$
start.penalty	numeric	-		$(-\infty, \infty)$
iter.max	integer	10		$[1, \infty)$
upper.margin	numeric	0.05		$[0, 1]$
unpen.index	untyped	-		-
standardize	logical	TRUE	TRUE, FALSE	-
penalty	numeric	-		$(-\infty, \infty)$
criterion	character	pscore	pscore, score, hpscore, hscore	-
stepsize.factor	numeric	1		$(-\infty, \infty)$
sf.scheme	character	sigmoid	sigmoid, linear	-
pendistmat	untyped	-		-
connected.index	untyped	-		-
x.is.01	logical	FALSE	TRUE, FALSE	-
return.score	logical	TRUE	TRUE, FALSE	-
trace	logical	FALSE	TRUE, FALSE	-
at.step	untyped	-		-

Installation

The package 'CoxBoost' is not on CRAN and has to be installed from GitHub using remotes::install_github("binderh/CoxBoost").

Prediction types

This learner returns three prediction types, using the internal predict.CoxBoost() function:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation.
crank: same as lp.
distr: a 2d survival matrix, with observations as rows and time points as columns. The internal transformation uses the Breslow estimator to compute the baseline hazard and compose the survival distributions from the lp predictions.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCVCoxboost

Methods

Public methods

LearnerSurvCVCoxboost$new()
LearnerSurvCVCoxboost$selected_features()
LearnerSurvCVCoxboost$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvCVCoxboost$new()

Method `selected_features()`

Returns the set of selected features which have non-zero coefficients. Calls the internal coef.CoxBoost() function.

Usage

LearnerSurvCVCoxboost$selected_features(at_step = NULL)

Arguments

at_step: (integer(1))
Which boosting step to get the coefficients for. If no step is given (default), the final boosting step is used.

Returns

(character()) vector of feature names.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvCVCoxboost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Binder, Harald, Allignol, Arthur, Schumacher, Martin, Beyersmann, Jan (2009). “Boosting for high-dimensional time-to-event data with competing risks.” Bioinformatics, 25(7), 890–896.

Examples


task = tsk("rats")
task$col_roles$feature = c("litter", "rx")
learner = lrn("surv.cv_coxboost", maxstepno = 20)
splits = partition(task)
learner$train(task, splits$train)
pred = learner$predict(task, splits$test)

task = tsk("rats")
task$col_roles$feature = c("litter", "rx")
learner = lrn("surv.cv_coxboost", maxstepno = 20)
splits = partition(task)
learner$train(task, splits$train)
pred = learner$predict(task, splits$test)

Cross-Validated GLM with Elastic Net Regularization Survival Learner

Description

Generalized linear models with elastic net regularization. Calls glmnet::cv.glmnet() from package glmnet.

Initial parameter values

family is set to "cox" and cannot be changed.

Prediction types

This learner returns three prediction types:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using glmnet::predict.cv.glmnet().
crank: same as lp.
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using glmnet::survfit.cv.glmnet(). Parameters stype and ctype relate to how lp predictions are transformed into survival predictions and are described in survival::survfit.coxph(). By default the Breslow estimator is used for computing the baseline hazard.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.cv_glmnet")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, glmnet

Parameters

Id	Type	Default	Levels	Range
alignment	character	lambda	lambda, fraction	-
alpha	numeric	1		$[0, 1]$
big	numeric	9.9e+35		$(-\infty, \infty)$
devmax	numeric	0.999		$[0, 1]$
dfmax	integer	-		$[0, \infty)$
eps	numeric	1e-06		$[0, 1]$
epsnr	numeric	1e-08		$[0, 1]$
exclude	untyped	-		-
exmx	numeric	250		$(-\infty, \infty)$
fdev	numeric	1e-05		$[0, 1]$
foldid	untyped	NULL		-
gamma	untyped	-		-
grouped	logical	TRUE	TRUE, FALSE	-
intercept	logical	TRUE	TRUE, FALSE	-
keep	logical	FALSE	TRUE, FALSE	-
lambda	untyped	-		-
lambda.min.ratio	numeric	-		$[0, 1]$
lower.limits	untyped	-Inf		-
maxit	integer	100000		$[1, \infty)$
mnlam	integer	5		$[1, \infty)$
mxit	integer	100		$[1, \infty)$
mxitnr	integer	25		$[1, \infty)$
nfolds	integer	10		$[3, \infty)$
nlambda	integer	100		$[1, \infty)$
offset	untyped	NULL		-
newoffset	untyped	-		-
parallel	logical	FALSE	TRUE, FALSE	-
penalty.factor	untyped	-		-
pmax	integer	-		$[0, \infty)$
pmin	numeric	1e-09		$[0, 1]$
prec	numeric	1e-10		$(-\infty, \infty)$
predict.gamma	numeric	gamma.1se		$(-\infty, \infty)$
relax	logical	FALSE	TRUE, FALSE	-
s	numeric	lambda.1se		$[0, \infty)$
standardize	logical	TRUE	TRUE, FALSE	-
standardize.response	logical	FALSE	TRUE, FALSE	-
thresh	numeric	1e-07		$[0, \infty)$
trace.it	integer	0		$[0, 1]$
type.gaussian	character	-	covariance, naive	-
type.logistic	character	Newton	Newton, modified.Newton	-
type.measure	character	deviance	deviance, C	-
type.multinomial	character	ungrouped	ungrouped, grouped	-
upper.limits	untyped	Inf		-
stype	integer	2		$[1, 2]$
ctype	integer	-		$[1, 2]$

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCVGlmnet

Methods

Public methods

LearnerSurvCVGlmnet$new()
LearnerSurvCVGlmnet$selected_features()
LearnerSurvCVGlmnet$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvCVGlmnet$new()

Method `selected_features()`

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerSurvCVGlmnet$selected_features(lambda = NULL)

Arguments

lambda: (numeric(1))
Custom lambda, defaults to the active lambda depending on parameter set.

Returns

(character()) of feature names.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvCVGlmnet$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01.

Examples


# Define the Learner
learner = mlr3::lrn("surv.cv_glmnet")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.cv_glmnet")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival DeepHit Learner

Description

Neural network 'Deephit' for survival analysis. Calls survivalmodels::deephit() from pacakge 'survivalmodels'.

Details

Custom nets can be used in this learner either using the survivalmodels::build_pytorch_net utility function or using torch via reticulate. The number of output channels depends on the number of discretised time-points, i.e. the parameters cuts or cutpoints.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.pycox() function.
crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.deephit")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6, reticulate

Parameters

Id	Type	Default	Levels	Range
frac	numeric	0		$[0, 1]$
cuts	integer	10		$[1, \infty)$
cutpoints	untyped	-		-
scheme	character	equidistant	equidistant, quantiles	-
cut_min	numeric	0		$[0, \infty)$
num_nodes	untyped	c(32L, 32L)		-
batch_norm	logical	TRUE	TRUE, FALSE	-
dropout	numeric	-		$[0, 1]$
activation	character	relu	celu, elu, gelu, glu, hardshrink, hardsigmoid, hardswish, hardtanh, relu6, leakyrelu, ...	-
custom_net	untyped	-		-
device	untyped	-		-
mod_alpha	numeric	0.2		$[0, 1]$
sigma	numeric	0.1		$[0, \infty)$
optimizer	character	adam	adadelta, adagrad, adam, adamax, adamw, asgd, rmsprop, rprop, sgd, sparse_adam	-
rho	numeric	0.9		$(-\infty, \infty)$
eps	numeric	1e-08		$(-\infty, \infty)$
lr	numeric	1		$(-\infty, \infty)$
weight_decay	numeric	0		$(-\infty, \infty)$
learning_rate	numeric	0.01		$(-\infty, \infty)$
lr_decay	numeric	0		$(-\infty, \infty)$
betas	untyped	c(0.9, 0.999)		-
amsgrad	logical	FALSE	TRUE, FALSE	-
lambd	numeric	1e-04		$[0, \infty)$
alpha	numeric	0.75		$[0, \infty)$
t0	numeric	1e+06		$(-\infty, \infty)$
momentum	numeric	0		$(-\infty, \infty)$
centered	logical	TRUE	TRUE, FALSE	-
etas	untyped	c(0.5, 1.2)		-
step_sizes	untyped	c(1e-06, 50)		-
dampening	numeric	0		$(-\infty, \infty)$
nesterov	logical	FALSE	TRUE, FALSE	-
batch_size	integer	256		$(-\infty, \infty)$
epochs	integer	1		$[1, \infty)$
verbose	logical	TRUE	TRUE, FALSE	-
num_workers	integer	0		$(-\infty, \infty)$
shuffle	logical	TRUE	TRUE, FALSE	-
best_weights	logical	FALSE	TRUE, FALSE	-
early_stopping	logical	FALSE	TRUE, FALSE	-
min_delta	numeric	0		$(-\infty, \infty)$
patience	integer	10		$(-\infty, \infty)$
interpolate	logical	FALSE	TRUE, FALSE	-
inter_scheme	character	const_hazard	const_hazard, const_pdf	-
sub	integer	10		$[1, \infty)$

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvDeephit

Methods

Public methods

LearnerSurvDeephit$new()
LearnerSurvDeephit$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvDeephit$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvDeephit$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Lee, Changhee, Zame, William, Yoon, Jinsung, Van Der Schaar, Mihaela (2018). “Deephit: A deep learning approach to survival analysis with competing risks.” In Proceedings of the AAAI conference on artificial intelligence, volume 32 number 1.

Examples

lrn("surv.deephit")
lrn("surv.deephit")

Survival DeepSurv Learner

Description

DeepSurv fits a neural network based on the partial likelihood from a Cox PH. Calls survivalmodels::deepsurv() from package 'survivalmodels'.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.pycox() function.
crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.deepsurv")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6, reticulate

Parameters

Id	Type	Default	Levels	Range
frac	numeric	0		$[0, 1]$
num_nodes	untyped	c(32L, 32L)		-
batch_norm	logical	TRUE	TRUE, FALSE	-
dropout	numeric	-		$[0, 1]$
activation	character	relu	celu, elu, gelu, glu, hardshrink, hardsigmoid, hardswish, hardtanh, relu6, leakyrelu, ...	-
device	untyped	-		-
optimizer	character	adam	adadelta, adagrad, adam, adamax, adamw, asgd, rmsprop, rprop, sgd, sparse_adam	-
rho	numeric	0.9		$(-\infty, \infty)$
eps	numeric	1e-08		$(-\infty, \infty)$
lr	numeric	1		$(-\infty, \infty)$
weight_decay	numeric	0		$(-\infty, \infty)$
learning_rate	numeric	0.01		$(-\infty, \infty)$
lr_decay	numeric	0		$(-\infty, \infty)$
betas	untyped	c(0.9, 0.999)		-
amsgrad	logical	FALSE	TRUE, FALSE	-
lambd	numeric	1e-04		$[0, \infty)$
alpha	numeric	0.75		$[0, \infty)$
t0	numeric	1e+06		$(-\infty, \infty)$
momentum	numeric	0		$(-\infty, \infty)$
centered	logical	TRUE	TRUE, FALSE	-
etas	untyped	c(0.5, 1.2)		-
step_sizes	untyped	c(1e-06, 50)		-
dampening	numeric	0		$(-\infty, \infty)$
nesterov	logical	FALSE	TRUE, FALSE	-
batch_size	integer	256		$(-\infty, \infty)$
epochs	integer	1		$[1, \infty)$
verbose	logical	TRUE	TRUE, FALSE	-
num_workers	integer	0		$(-\infty, \infty)$
shuffle	logical	TRUE	TRUE, FALSE	-
best_weights	logical	FALSE	TRUE, FALSE	-
early_stopping	logical	FALSE	TRUE, FALSE	-
min_delta	numeric	0		$(-\infty, \infty)$
patience	integer	10		$(-\infty, \infty)$

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvDeepsurv

Methods

Public methods

LearnerSurvDeepsurv$new()
LearnerSurvDeepsurv$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvDeepsurv$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvDeepsurv$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Katzman, L J, Shaham, Uri, Cloninger, Alexander, Bates, Jonathan, Jiang, Tingting, Kluger, Yuval (2018). “DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.” BMC medical research methodology, 18(1), 1–12.

Examples

lrn("surv.deepsurv")
lrn("surv.deepsurv")

Survival DNNSurv Learner

Description

Fits a neural network based on pseudo-conditional survival probabilities. Calls survivalmodels::dnnsurv() from package 'survivalmodels'.

Details

Custom nets can be used in this learner either using the survivalmodels::build_keras_net utility function or using keras. The number of output channels should be of length 1 and number of input channels is the number of features plus number of cuts.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.dnnsurv() function.
crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.dnnsurv")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, keras, pseudo, tensorflow, distr6

Parameters

Id	Type	Default	Levels	Range
cuts	integer	5		$[1, \infty)$
cutpoints	untyped	-		-
custom_model	untyped	-		-
optimizer	character	adam	adadelta, adagrad, adamax, adam, nadam, rmsprop, sgd	-
lr	numeric	0.02		$[0, \infty)$
beta_1	numeric	0.9		$[0, 1]$
beta_2	numeric	0.999		$[0, 1]$
epsilon	numeric	-		$[0, \infty)$
decay	numeric	0		$[0, \infty)$
clipnorm	numeric	-		$(-\infty, \infty)$
clipvalue	numeric	-		$(-\infty, \infty)$
momentum	numeric	0		$[0, \infty)$
nesterov	logical	FALSE	TRUE, FALSE	-
loss_weights	untyped	-		-
weighted_metrics	untyped	-		-
early_stopping	logical	FALSE	TRUE, FALSE	-
min_delta	numeric	0		$[0, \infty)$
patience	integer	0		$[0, \infty)$
verbose	integer	0		$[0, 2]$
baseline	numeric	-		$(-\infty, \infty)$
restore_best_weights	logical	FALSE	TRUE, FALSE	-
batch_size	integer	32		$[1, \infty)$
epochs	integer	10		$[1, \infty)$
validation_split	numeric	0		$[0, 1]$
shuffle	logical	TRUE	TRUE, FALSE	-
sample_weight	untyped	-		-
initial_epoch	integer	0		$[0, \infty)$
steps_per_epoch	integer	-		$[1, \infty)$
validation_steps	integer	-		$[1, \infty)$
steps	integer	-		$[0, \infty)$
callbacks	untyped	-		-
rho	numeric	0.95		$(-\infty, \infty)$
global_clipnorm	numeric	-		$(-\infty, \infty)$
use_ema	logical	-	TRUE, FALSE	-
ema_momentum	numeric	0.99		$(-\infty, \infty)$
ema_overwrite_frequency	numeric	-		$(-\infty, \infty)$
jit_compile	logical	TRUE	TRUE, FALSE	-
initial_accumultator_value	numeric	0.1		$(-\infty, \infty)$
amsgrad	logical	FALSE	TRUE, FALSE	-
lr_power	numeric	-0.5		$(-\infty, \infty)$
l1_regularization_strength	numeric	0		$[0, \infty)$
l2_regularization_strength	numeric	0		$[0, \infty)$
l2_shrinkage_regularization_strength	numeric	0		$[0, \infty)$
beta	numeric	0		$(-\infty, \infty)$
centered	logical	FALSE	TRUE, FALSE	-

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Initial parameter values

verbose is initialized to 0.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvDNNSurv

Methods

Public methods

LearnerSurvDNNSurv$new()
LearnerSurvDNNSurv$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvDNNSurv$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvDNNSurv$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Zhao, Lili, Feng, Dai (2019). “Dnnsurv: Deep neural networks for survival analysis using pseudo values.” arXiv preprint arXiv:1908.02337.

Examples

lrn("surv.dnnsurv")
lrn("surv.dnnsurv")

Survival Flexible Parametric Spline Learner

Description

Flexible parametric spline learner. Calls flexsurv::flexsurvspline() from flexsurv.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.flexible")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, flexsurv, pracma

Parameters

Id	Type	Default	Levels	Range
bhazard	untyped	-		-
k	integer	0		$[0, \infty)$
knots	untyped	-		-
bknots	untyped	-		-
scale	character	hazard	hazard, odds, normal	-
timescale	character	log	log, identity	-
spline	character	rp	rp, splines2ns	-
inits	untyped	-		-
rtrunc	untyped	-		-
fixedpars	untyped	-		-
cl	numeric	0.95		$[0, 1]$
maxiter	integer	30		$(-\infty, \infty)$
rel.tolerance	numeric	1e-09		$(-\infty, \infty)$
toler.chol	numeric	1e-10		$(-\infty, \infty)$
debug	integer	0		$[0, 1]$
outer.max	integer	10		$(-\infty, \infty)$

Prediction types

This learner returns three prediction types:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using flexsurv::flexsurvspline() and the estimated coefficients. For fitted coefficients, $\hat{\beta} = (\hat{\beta_0},...,\hat{\beta_P})$ , and the test data covariates $X^T = (X_0,...,X_P)^T$ , where $X_0$ is a column of $1$ s, the linear predictor vector is $lp = \hat{\beta} X^T$ .
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using predict.flexsurvreg().
crank: same as lp.

Initial parameter values

k:
- Actual default: 0
- Initial value: 1
- Reason for change: The default value of 0 is equivalent to, and a much less efficient implementation of, LearnerSurvParametric.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvFlexible

Methods

Public methods

LearnerSurvFlexible$new()
LearnerSurvFlexible$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvFlexible$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvFlexible$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Royston, Patrick, Parmar, KB M (2002). “Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.” Statistics in medicine, 21(15), 2175–2197.

Examples

lrn("surv.flexible")
lrn("surv.flexible")

Boosted Generalized Additive Survival Learner

Description

Fits a generalized additive survival model using a boosting algorithm. Calls mboost::gamboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.gamboost")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma

Parameters

Id	Type	Default	Levels	Range
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
offset	numeric	-		$(-\infty, \infty)$
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		$[0, \infty)$
nu	numeric	0.1		$[0, 1]$
risk	character	inbag	inbag, oobag, none	-
stopintern	untyped	FALSE		-
trace	logical	FALSE	TRUE, FALSE	-
oobweights	untyped	NULL		-
baselearner	character	bbs	bbs, bols, btree	-
dfbase	integer	4		$[0, \infty)$
sigma	numeric	0.1		$[0, 1]$
ipcw	untyped	1		-
na.action	untyped	stats::na.omit		-

Prediction types

This learner returns two to three prediction types:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using mboost::predict.gamboost(). If the family parameter is not "coxph", -lp is returned, since non-coxph families represent AFT-style distributions where lower lp values indicate higher risk.
crank: same as lp.
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using mboost::survFit(). This prediction type is present only when the family distribution parameter is equal to "coxph" (default). By default the Breslow estimator is used for computing the baseline hazard.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGAMBoost

Methods

Public methods

LearnerSurvGAMBoost$new()
LearnerSurvGAMBoost$importance()
LearnerSurvGAMBoost$selected_features()
LearnerSurvGAMBoost$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvGAMBoost$new()

Method `importance()`

The importance scores are extracted with the function mboost::varimp() with the default arguments.

Usage

LearnerSurvGAMBoost$importance()

Returns

Named numeric().

Method `selected_features()`

Selected features are extracted with the function mboost::variable.names.mboost(), with used.only = TRUE.

Usage

LearnerSurvGAMBoost$selected_features()

Returns

character().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvGAMBoost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

Examples

lrn("surv.gamboost")
lrn("surv.gamboost")

Survival Gradient Boosting Machine Learner

Description

Gradient Boosting for Survival Analysis. Calls gbm::gbm() from gbm.

Prediction types

This learner returns two prediction types, using the internal predict.gbm() function:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation.
crank: same as lp.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.gbm")

Meta Information

Task type: “surv”
Predict Types: “crank”, “lp”
Feature Types: “integer”, “numeric”, “factor”, “ordered”
Required Packages: mlr3, mlr3proba, mlr3extralearners, gbm

Parameters

Id	Type	Default	Levels	Range
distribution	character	coxph	coxph	-
n.trees	integer	100		$[1, \infty)$
cv.folds	integer	0		$[0, \infty)$
interaction.depth	integer	1		$[1, \infty)$
n.minobsinnode	integer	10		$[1, \infty)$
shrinkage	numeric	0.001		$[0, \infty)$
bag.fraction	numeric	0.5		$[0, 1]$
train.fraction	numeric	1		$[0, 1]$
keep.data	logical	FALSE	TRUE, FALSE	-
verbose	logical	FALSE	TRUE, FALSE	-
var.monotone	untyped	-		-
n.cores	integer	1		$(-\infty, \infty)$
single.tree	logical	FALSE	TRUE, FALSE	-

Initial parameter values

distribution:
Actual default: "bernoulli"
Adjusted default: "coxph"
Reason for change: This is the only distribution available for survival.
keep.data:
- Actual default: TRUE
- Adjusted default: FALSE
- Reason for change: keep.data = FALSE saves memory during model fitting.
n.cores:
- Actual default: NULL
- Adjusted default: 1
- Reason for change: Suppressing the automatic internal parallelization if cv.folds > 0 and avoid threading conflicts with future.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGBM

Methods

Public methods

LearnerSurvGBM$new()
LearnerSurvGBM$importance()
LearnerSurvGBM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvGBM$new()

Method `importance()`

The importance scores are extracted from the model slot variable.importance.

Usage

LearnerSurvGBM$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvGBM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Friedman, H J (2002). “Stochastic gradient boosting.” Computational statistics & data analysis, 38(4), 367–378.

Examples


# Define the Learner
learner = mlr3::lrn("surv.gbm")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.gbm")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Boosted Generalized Linear Survival Learner

Description

Fits a generalized linear survival model using a boosting algorithm. Calls mboost::glmboost() from mboost.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.glmboost")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost, pracma

Parameters

Id	Type	Default	Levels	Range
offset	numeric	-		$(-\infty, \infty)$
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		$[0, \infty)$
nu	numeric	0.1		$[0, 1]$
risk	character	inbag	inbag, oobag, none	-
oobweights	untyped	NULL		-
stopintern	logical	FALSE	TRUE, FALSE	-
trace	logical	FALSE	TRUE, FALSE	-
sigma	numeric	0.1		$[0, 1]$
ipcw	untyped	1		-
na.action	untyped	stats::na.omit		-
contrasts.arg	untyped	-		-

Prediction types

This learner returns two to three prediction types:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using mboost::predict.glmboost(). If the family parameter is not "coxph", -lp is returned, since non-coxph families represent AFT-style distributions where lower lp values indicate higher risk.
crank: same as lp.
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using mboost::survFit(). This prediction type is present only when the family distribution parameter is equal to "coxph" (default). By default the Breslow estimator is used for computing the baseline hazard.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGLMBoost

Methods

Public methods

LearnerSurvGLMBoost$new()
LearnerSurvGLMBoost$importance()
LearnerSurvGLMBoost$selected_features()
LearnerSurvGLMBoost$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvGLMBoost$new()

Method `importance()`

Importance scores are extracted with the function mboost::varimp() and represent a feature's individual contribution to the risk reduction per boosting step of the fitted model. The higher the risk reduction, the larger the feature importance.

Note: Importance is supported only for datasets with numeric features, as the presence of factors with multiple levels makes it difficult to get the original feature names.

Usage

LearnerSurvGLMBoost$importance()

Returns

Named numeric().

Method `selected_features()`

Selected features are extracted with the function mboost::coef.glmboost() which by default returns features with non-zero coefficients and for the final number of boosting iterations.

Note: Selected features can be retrieved only for datasets with numeric features, as the presence of factors with multiple levels makes it difficult to get the original feature names.

Usage

LearnerSurvGLMBoost$selected_features()

Returns

character().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvGLMBoost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

Examples


# Define the Learner
learner = mlr3::lrn("surv.glmboost")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.glmboost")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

GLM with Elastic Net Regularization Survival Learner

Description

Generalized linear models with elastic net regularization. Calls glmnet::glmnet() from package glmnet.

Initial parameter values

family is set to "cox" and cannot be changed.

Prediction types

This learner returns three prediction types:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using glmnet::predict.coxnet().
crank: same as lp.
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using glmnet::survfit.coxnet(). Parameters stype and ctype relate to how lp predictions are transformed into survival predictions and are described in survival::survfit.coxph(). By default the Breslow estimator is used for computing the baseline hazard.

Caution: This learner is different to learners calling glmnet::cv.glmnet() in that it does not use the internal optimization of parameter lambda. Instead, lambda needs to be tuned by the user (e.g., via mlr3tuning). When lambda is tuned, the glmnet will be trained for each tuning iteration. While fitting the whole path of lambdas would be more efficient, as is done by default in glmnet::glmnet(), tuning/selecting the parameter at prediction time (using parameter s) is currently not supported in mlr3 (at least not in efficient manner). Tuning the s parameter is, therefore, currently discouraged.

When the data are i.i.d. and efficiency is key, we recommend using the respective auto-tuning counterpart in mlr_learners_surv.cv_glmnet(). However, in some situations this is not applicable, usually when data are imbalanced or not i.i.d. (longitudinal, time-series) and tuning requires custom resampling strategies (blocked design, stratification).

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.glmnet")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, glmnet

Parameters

Id	Type	Default	Levels	Range
alignment	character	lambda	lambda, fraction	-
alpha	numeric	1		$[0, 1]$
big	numeric	9.9e+35		$(-\infty, \infty)$
devmax	numeric	0.999		$[0, 1]$
dfmax	integer	-		$[0, \infty)$
eps	numeric	1e-06		$[0, 1]$
epsnr	numeric	1e-08		$[0, 1]$
exact	logical	FALSE	TRUE, FALSE	-
exclude	untyped	-		-
exmx	numeric	250		$(-\infty, \infty)$
fdev	numeric	1e-05		$[0, 1]$
gamma	untyped	-		-
grouped	logical	TRUE	TRUE, FALSE	-
intercept	logical	TRUE	TRUE, FALSE	-
keep	logical	FALSE	TRUE, FALSE	-
lambda	untyped	-		-
lambda.min.ratio	numeric	-		$[0, 1]$
lower.limits	untyped	-Inf		-
maxit	integer	100000		$[1, \infty)$
mnlam	integer	5		$[1, \infty)$
mxit	integer	100		$[1, \infty)$
mxitnr	integer	25		$[1, \infty)$
newoffset	untyped	-		-
nlambda	integer	100		$[1, \infty)$
offset	untyped	NULL		-
parallel	logical	FALSE	TRUE, FALSE	-
penalty.factor	untyped	-		-
pmax	integer	-		$[0, \infty)$
pmin	numeric	1e-09		$[0, 1]$
prec	numeric	1e-10		$(-\infty, \infty)$
predict.gamma	numeric	gamma.1se		$(-\infty, \infty)$
relax	logical	FALSE	TRUE, FALSE	-
s	numeric	0.01		$[0, \infty)$
standardize	logical	TRUE	TRUE, FALSE	-
thresh	numeric	1e-07		$[0, \infty)$
trace.it	integer	0		$[0, 1]$
type.logistic	character	Newton	Newton, modified.Newton	-
type.multinomial	character	ungrouped	ungrouped, grouped	-
upper.limits	untyped	Inf		-
stype	integer	2		$[1, 2]$
ctype	integer	-		$[1, 2]$

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGlmnet

Methods

Public methods

LearnerSurvGlmnet$new()
LearnerSurvGlmnet$selected_features()
LearnerSurvGlmnet$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvGlmnet$new()

Method `selected_features()`

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerSurvGlmnet$selected_features(lambda = NULL)

Arguments

lambda: (numeric(1))
Custom lambda, defaults to the active lambda depending on parameter set.

Returns

(character()) of feature names.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvGlmnet$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Examples


# Define the Learner
learner = mlr3::lrn("surv.glmnet")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.glmnet")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Logistic-Hazard Learner

Description

Survival logistic hazard learner. Calls survivalmodels::loghaz() from package 'survivalmodels'.

Details

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.pycox() function.
crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.loghaz")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6, reticulate

Parameters

Id	Type	Default	Levels	Range
frac	numeric	0		$[0, 1]$
cuts	integer	10		$[1, \infty)$
cutpoints	untyped	-		-
scheme	character	equidistant	equidistant, quantiles	-
cut_min	numeric	0		$[0, \infty)$
num_nodes	untyped	c(32L, 32L)		-
batch_norm	logical	TRUE	TRUE, FALSE	-
dropout	numeric	-		$[0, 1]$
activation	character	relu	celu, elu, gelu, glu, hardshrink, hardsigmoid, hardswish, hardtanh, relu6, leakyrelu, ...	-
custom_net	untyped	-		-
device	untyped	-		-
optimizer	character	adam	adadelta, adagrad, adam, adamax, adamw, asgd, rmsprop, rprop, sgd, sparse_adam	-
rho	numeric	0.9		$(-\infty, \infty)$
eps	numeric	1e-08		$(-\infty, \infty)$
lr	numeric	1		$(-\infty, \infty)$
weight_decay	numeric	0		$(-\infty, \infty)$
learning_rate	numeric	0.01		$(-\infty, \infty)$
lr_decay	numeric	0		$(-\infty, \infty)$
betas	untyped	c(0.9, 0.999)		-
amsgrad	logical	FALSE	TRUE, FALSE	-
lambd	numeric	1e-04		$[0, \infty)$
alpha	numeric	0.75		$[0, \infty)$
t0	numeric	1e+06		$(-\infty, \infty)$
momentum	numeric	0		$(-\infty, \infty)$
centered	logical	TRUE	TRUE, FALSE	-
etas	untyped	c(0.5, 1.2)		-
step_sizes	untyped	c(1e-06, 50)		-
dampening	numeric	0		$(-\infty, \infty)$
nesterov	logical	FALSE	TRUE, FALSE	-
batch_size	integer	256		$(-\infty, \infty)$
epochs	integer	1		$[1, \infty)$
verbose	logical	TRUE	TRUE, FALSE	-
num_workers	integer	0		$(-\infty, \infty)$
shuffle	logical	TRUE	TRUE, FALSE	-
best_weights	logical	FALSE	TRUE, FALSE	-
early_stopping	logical	FALSE	TRUE, FALSE	-
min_delta	numeric	0		$(-\infty, \infty)$
patience	integer	10		$(-\infty, \infty)$
interpolate	logical	FALSE	TRUE, FALSE	-
inter_scheme	character	const_hazard	const_hazard, const_pdf	-
sub	integer	10		$[1, \infty)$

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvLogisticHazard

Methods

Public methods

LearnerSurvLogisticHazard$new()
LearnerSurvLogisticHazard$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvLogisticHazard$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvLogisticHazard$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Gensheimer, F M, Narasimhan, BA (2018). “Simple discrete-time survival model for neural networks.” arXiv.

Kvamme, Håvard, Borgan Ø, Scheel I (2019). “Time-to-event prediction with neural networks and Cox regression.” arXiv preprint arXiv:1907.00825.

Examples

lrn("surv.loghaz")
lrn("surv.loghaz")

Boosted Generalized Additive Survival Learner

Description

Model-based boosting for survival analysis. Calls mboost::mboost() from mboost.

Details

distr prediction made by mboost::survFit().

Prediction types

This learner returns two to three prediction types:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using mboost::predict.mboost(). If the family parameter is not "coxph", -lp is returned, since non-coxph families represent AFT-style distributions where lower lp values indicate higher risk.
crank: same as lp.
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using mboost::survFit(). This prediction type is present only when the family distribution parameter is equal to "coxph" (default). By default the Breslow estimator is used for computing the baseline hazard.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.mboost")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, mboost

Parameters

Id	Type	Default	Levels	Range
family	character	coxph	coxph, weibull, loglog, lognormal, gehan, cindex, custom	-
custom.family	untyped	-		-
nuirange	untyped	c(0, 100)		-
offset	numeric	-		$(-\infty, \infty)$
center	logical	TRUE	TRUE, FALSE	-
mstop	integer	100		$[0, \infty)$
nu	numeric	0.1		$[0, 1]$
risk	character	inbag	inbag, oobag, none	-
stopintern	logical	FALSE	TRUE, FALSE	-
trace	logical	FALSE	TRUE, FALSE	-
oobweights	untyped	NULL		-
baselearner	character	bbs	bbs, bols, btree	-
sigma	numeric	0.1		$[0, 1]$
ipcw	untyped	1		-
na.action	untyped	stats::na.omit		-

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvMBoost

Methods

Public methods

LearnerSurvMBoost$new()
LearnerSurvMBoost$importance()
LearnerSurvMBoost$selected_features()
LearnerSurvMBoost$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvMBoost$new()

Method `importance()`

The importance scores are extracted with the function mboost::varimp() with the default arguments.

Usage

LearnerSurvMBoost$importance()

Returns

Named numeric().

Method `selected_features()`

Selected features are extracted with the function mboost::variable.names.mboost(), with used.only = TRUE.

Usage

LearnerSurvMBoost$selected_features()

Returns

character().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvMBoost$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Bühlmann, Peter, Yu, Bin (2003). “Boosting with the L 2 loss: regression and classification.” Journal of the American Statistical Association, 98(462), 324–339.

Examples

lrn("surv.mboost")
lrn("surv.mboost")

Survival Nelson-Aalen Estimator Learner

Description

Non-parametric estimator of the cumulative hazard rate function. Calls survival::survfit() from survival.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. The cumulative hazard $H(t)$ is calculated using the train data and the default parameters of the survival::survfit() function, i.e. ctype = 1, which uses the Nelson-Aalen formula. Then for each test observation the survival curve is $S(t) = \exp{(-H(t))}$ .
crank: the expected mortality using mlr3proba::.surv_return().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.nelson")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survival, pracma

Parameters

Empty ParamSet

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvNelson

Methods

Public methods

LearnerSurvNelson$new()
LearnerSurvNelson$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvNelson$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvNelson$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Nelson, Wayne (1969). “Hazard plotting for incomplete failure data.” Journal of Quality Technology, 1(1), 27–52.

Nelson, Wayne (1972). “Theory and applications of hazard plotting for censored failure data.” Technometrics, 14(4), 945–966.

Aalen, Odd (1978). “Nonparametric inference for a family of counting processes.” The Annals of Statistics, 701–726.

Examples


# Define the Learner
learner = mlr3::lrn("surv.nelson")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.nelson")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Fully Parametric Learner

Description

Parametric survival model. Calls survivalmodels::parametric() from package 'survivalmodels'.

Details

This learner allows you to choose a distribution and a model form to compose a predicted survival probability distribution $S(t)$ .

The predict method is implemented in survivalmodels::predict.parametric(). Our implementation is more efficient for composition to distributions than survival::predict.survreg().

The available model forms with their respective survival functions, are as follows:

Accelerated Failure Time (aft)

$S(t) = S_0(\frac{t}{exp(lp)})$
Proportional Hazards (ph)

$S(t) = S_0(t)^{exp(lp)}$
Proportional Odds (po)

$S(t) = \frac{S_0(t)}{exp(-lp) + (1-exp(-lp)) S_0(t)}$
Tobit (tobit)

$S(t) = 1 - \Phi((t - lp)/s)$

where $S_0$ is the estimated baseline survival distribution (in this case with a given parametric form), $lp$ is the predicted linear predictor, $\Phi$ is the cdf of a $N(0, 1)$ distribution, and $s$ is the fitted scale parameter.

Whilst any combination of distribution and model form is possible, this does not mean it will necessarily create a sensible or interpretable prediction. The following combinations are 'sensible' (we note that ones mostly used in the literature):

dist = "gaussian"; form = "tobit";
dist = "weibull"; form = "ph"; (fairly used)
dist = "exponential"; form = "ph";
dist = "weibull"; form = "aft"; (fairly used, default option)
dist = "exponential"; form = "aft";
dist = "loglogistic"; form = "aft"; (fairly used)
dist = "lognormal"; form = "aft";
dist = "loglogistic"; form = "po";

Prediction types

This learner returns three prediction types:

lp: a vector of linear predictors (relative risk scores), one per test observation. lp is predicted using the formula $lp = X\beta$ where $X$ are the variables in the test data set and $\beta$ are the fitted coefficients.
crank: same as lp.
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. The distribution distr is composed using the lp predictions and specifying a model form in the form hyper-parameter, see Details. The survival matrix uses the unique time points from the training set. The parameter ntime allows to adjust the granularity of these time points to any number (e.g. 150).

Custom mlr3 parameters

discrete determines the class of the returned survival probability distribution. If FALSE, vectorized continuous probability distributions are returned using distr6::VectorDistribution, otherwise a distr6::Matdist object, which is faster for calculating survival measures that require a distr prediction type. Default option is TRUE.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.parametric")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survival, pracma

Parameters

Id	Type	Default	Levels	Range
form	character	aft	aft, ph, po, tobit	-
na.action	untyped	-		-
dist	character	weibull	weibull, exponential, gaussian, lognormal, loglogistic	-
parms	untyped	-		-
init	untyped	-		-
scale	numeric	0		$[0, \infty)$
maxiter	integer	30		$(-\infty, \infty)$
rel.tolerance	numeric	1e-09		$(-\infty, \infty)$
toler.chol	numeric	1e-10		$(-\infty, \infty)$
debug	integer	0		$[0, 1]$
outer.max	integer	10		$(-\infty, \infty)$
robust	logical	FALSE	TRUE, FALSE	-
score	logical	FALSE	TRUE, FALSE	-
cluster	untyped	-		-
discrete	logical	-	TRUE, FALSE	-
ntime	integer	NULL		$[1, \infty)$
round_time	integer	2		$[0, \infty)$

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvParametric

Methods

Public methods

LearnerSurvParametric$new()
LearnerSurvParametric$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class. returned risk from survivalmodels is hp-style ie higher value => higher risk

Usage

LearnerSurvParametric$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvParametric$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

bblodfon

References

Kalbfleisch, D J, Prentice, L R (2011). The statistical analysis of failure time data. John Wiley & Sons.

Examples


# Define the Learner
learner = mlr3::lrn("surv.parametric")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.parametric")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival PC-Hazard Learner

Description

Logistic-Hazard fits a discrete neural network based on a cross-entropy loss and predictions of a discrete hazard function, also known as Nnet-Survival. Calls survivalmodels::pchazard() from package 'survivalmodels'.

Details

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal survivalmodels::predict.pycox() function.
crank: the expected mortality using survivalmodels::surv_to_risk().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.pchazard")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalmodels, distr6, reticulate

Parameters

Id	Type	Default	Levels	Range
frac	numeric	0		$[0, 1]$
cuts	integer	10		$[1, \infty)$
cutpoints	untyped	-		-
scheme	character	equidistant	equidistant, quantiles	-
cut_min	numeric	0		$[0, \infty)$
num_nodes	untyped	c(32L, 32L)		-
batch_norm	logical	TRUE	TRUE, FALSE	-
reduction	character	mean	mean, none, sum	-
dropout	numeric	-		$[0, 1]$
activation	character	relu	celu, elu, gelu, glu, hardshrink, hardsigmoid, hardswish, hardtanh, relu6, leakyrelu, ...	-
custom_net	untyped	-		-
device	untyped	-		-
optimizer	character	adam	adadelta, adagrad, adam, adamax, adamw, asgd, rmsprop, rprop, sgd, sparse_adam	-
rho	numeric	0.9		$(-\infty, \infty)$
eps	numeric	1e-08		$(-\infty, \infty)$
lr	numeric	1		$(-\infty, \infty)$
weight_decay	numeric	0		$(-\infty, \infty)$
learning_rate	numeric	0.01		$(-\infty, \infty)$
lr_decay	numeric	0		$(-\infty, \infty)$
betas	untyped	c(0.9, 0.999)		-
amsgrad	logical	FALSE	TRUE, FALSE	-
lambd	numeric	1e-04		$[0, \infty)$
alpha	numeric	0.75		$[0, \infty)$
t0	numeric	1e+06		$(-\infty, \infty)$
momentum	numeric	0		$(-\infty, \infty)$
centered	logical	TRUE	TRUE, FALSE	-
etas	untyped	c(0.5, 1.2)		-
step_sizes	untyped	c(1e-06, 50)		-
dampening	numeric	0		$(-\infty, \infty)$
nesterov	logical	FALSE	TRUE, FALSE	-
batch_size	integer	256		$(-\infty, \infty)$
epochs	integer	1		$[1, \infty)$
verbose	logical	TRUE	TRUE, FALSE	-
num_workers	integer	0		$(-\infty, \infty)$
shuffle	logical	TRUE	TRUE, FALSE	-
best_weights	logical	FALSE	TRUE, FALSE	-
early_stopping	logical	FALSE	TRUE, FALSE	-
min_delta	numeric	0		$(-\infty, \infty)$
patience	integer	10		$(-\infty, \infty)$
interpolate	logical	FALSE	TRUE, FALSE	-
sub	integer	10		$[1, \infty)$

Installation

Package 'survivalmodels' is not on CRAN and has to be install from GitHub via remotes::install_github("RaphaelS1/survivalmodels").

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvPCHazard

Methods

Public methods

LearnerSurvPCHazard$new()
LearnerSurvPCHazard$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvPCHazard$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvPCHazard$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Kvamme, Håvard, Borgan Ø (2019). “Continuous and discrete-time survival prediction with neural networks.” arXiv preprint arXiv:1910.06724.

Examples

lrn("surv.pchazard")
lrn("surv.pchazard")

Survival L1 and L2 Penalized Cox Learner

Description

Penalized (L1 and L2) Cox Proportional Hazards model. Calls penalized::penalized() from penalized.

Details

The penalized and unpenalized arguments in the learner are implemented slightly differently than in penalized::penalized(). Here, there is no parameter for penalized but instead it is assumed that every variable is penalized unless stated in the unpenalized parameter.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using the internal penalized::predict() function. By default the Breslow estimator penalized::breslow() is used for computing the baseline hazard.
crank: the expected mortality using mlr3proba::.surv_return().

Initial parameter values

trace is set to "FALSE" to disable printing output during model training.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.penalized")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, penalized, pracma

Parameters

Id	Type	Default	Levels	Range
unpenalized	untyped	-		-
lambda1	untyped	0		-
lambda2	untyped	0		-
positive	logical	FALSE	TRUE, FALSE	-
fusedl	logical	FALSE	TRUE, FALSE	-
startbeta	numeric	-		$(-\infty, \infty)$
startgamma	numeric	-		$(-\infty, \infty)$
steps	integer	1		$[1, \infty)$
epsilon	numeric	1e-10		$[0, 1]$
maxiter	integer	-		$[1, \infty)$
standardize	logical	FALSE	TRUE, FALSE	-
trace	logical	TRUE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvPenalized

Methods

Public methods

LearnerSurvPenalized$new()
LearnerSurvPenalized$selected_features()
LearnerSurvPenalized$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvPenalized$new()

Method `selected_features()`

Selected features are extracted with the method coef() of the S4 model object, see penalized::penfit(). By default it returns features with non-zero coefficients.

Note: Selected features can be retrieved only for datasets with numeric features, as the presence of factors with multiple levels makes it difficult to get the original feature names.

Usage

LearnerSurvPenalized$selected_features()

Returns

character().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvPenalized$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Goeman, J J (2010). “L1 penalized estimation in the Cox proportional hazards model.” Biometrical journal, 52(1), 70–84.

Examples


# Define the Learner
learner = mlr3::lrn("surv.penalized")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.penalized")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Priority Lasso Learner

Description

Patient outcome prediction based on multi-omics data taking practitioners’ preferences into account. Calls prioritylasso::prioritylasso() from prioritylasso. Many parameters for this survival learner are the same as mlr_learners_surv.cv_glmnet as prioritylasso() calls glmnet::cv.glmnet() during training phase. Note that prioritylasso() has ways to deal with block-wise missing data, but this feature is not supported currently.

Prediction types

This learner returns three prediction types:

lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using prioritylasso::predict.prioritylasso().
crank: same as lp.
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using mlr3proba::breslow() where the Breslow estimator is used for computing the baseline hazard.

Initial parameter values

family is set to "cox" for the Cox survival objective and cannot be changed
type.measure set to "deviance" (cross-validation measure)

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.priority_lasso")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3proba, prioritylasso

Parameters

Id	Type	Default	Levels	Range
blocks	untyped	-		-
max.coef	untyped	NULL		-
block1.penalization	logical	TRUE	TRUE, FALSE	-
lambda.type	character	lambda.min	lambda.min, lambda.1se	-
standardize	logical	TRUE	TRUE, FALSE	-
nfolds	integer	5		$[1, \infty)$
foldid	untyped	NULL		-
cvoffset	logical	FALSE	TRUE, FALSE	-
cvoffsetnfolds	integer	10		$[1, \infty)$
return.x	logical	TRUE	TRUE, FALSE	-
include.allintercepts	logical	FALSE	TRUE, FALSE	-
use.blocks	untyped	"all"		-
alignment	character	lambda	lambda, fraction	-
alpha	numeric	1		$[0, 1]$
big	numeric	9.9e+35		$(-\infty, \infty)$
devmax	numeric	0.999		$[0, 1]$
dfmax	integer	-		$[0, \infty)$
eps	numeric	1e-06		$[0, 1]$
epsnr	numeric	1e-08		$[0, 1]$
exclude	untyped	-		-
exmx	numeric	250		$(-\infty, \infty)$
fdev	numeric	1e-05		$[0, 1]$
gamma	untyped	-		-
grouped	logical	TRUE	TRUE, FALSE	-
intercept	logical	TRUE	TRUE, FALSE	-
keep	logical	FALSE	TRUE, FALSE	-
lambda	untyped	-		-
lambda.min.ratio	numeric	-		$[0, 1]$
lower.limits	untyped	-Inf		-
maxit	integer	100000		$[1, \infty)$
mnlam	integer	5		$[1, \infty)$
mxit	integer	100		$[1, \infty)$
mxitnr	integer	25		$[1, \infty)$
nlambda	integer	100		$[1, \infty)$
offset	untyped	NULL		-
parallel	logical	FALSE	TRUE, FALSE	-
penalty.factor	untyped	-		-
pmax	integer	-		$[0, \infty)$
pmin	numeric	1e-09		$[0, 1]$
prec	numeric	1e-10		$(-\infty, \infty)$
standardize.response	logical	FALSE	TRUE, FALSE	-
thresh	numeric	1e-07		$[0, \infty)$
trace.it	integer	0		$[0, 1]$
type.gaussian	character	-	covariance, naive	-
type.logistic	character	Newton	Newton, modified.Newton	-
type.multinomial	character	ungrouped	ungrouped, grouped	-
upper.limits	untyped	Inf		-
relax	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvPriorityLasso

Methods

Public methods

LearnerSurvPriorityLasso$new()
LearnerSurvPriorityLasso$selected_features()
LearnerSurvPriorityLasso$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvPriorityLasso$new()

Method `selected_features()`

Selected features, i.e. those where the coefficient is non-zero.

Usage

LearnerSurvPriorityLasso$selected_features()

Returns

character().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvPriorityLasso$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

HarutyunyanLiana

References

Examples


# Define a Task
task = tsk("grace")
# Create train and test set
ids = partition(task)
# check task's features
task$feature_names
# partition features to 2 blocks
blocks = list(bl1 = 1:3, bl2 = 4:6)
# define learner
learner = lrn("surv.priority_lasso", blocks = blocks, block1.penalization = FALSE,
              lambda.type = "lambda.1se", standardize = TRUE, nfolds = 5)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# selected features
learner$selected_features()
# Make predictions for the test observations
pred = learner$predict(task, row_ids = ids$test)
pred
# Score the predictions
pred$score()

# Define a Task
task = tsk("grace")
# Create train and test set
ids = partition(task)
# check task's features
task$feature_names
# partition features to 2 blocks
blocks = list(bl1 = 1:3, bl2 = 4:6)
# define learner
learner = lrn("surv.priority_lasso", blocks = blocks, block1.penalization = FALSE,
              lambda.type = "lambda.1se", standardize = TRUE, nfolds = 5)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
# selected features
learner$selected_features()
# Make predictions for the test observations
pred = learner$predict(task, row_ids = ids$test)
pred
# Score the predictions
pred$score()

Ranger Survival Learner

Description

Random survival forest. Calls ranger::ranger() from package ranger.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and (unique event) time points in columns. Calculated using the internal ranger::predict.ranger() function.
crank: the expected mortality using mlr3proba::.surv_return().

Custom mlr3 parameters

mtry: This hyperparameter can alternatively be set via our hyperparameter mtry.ratio as mtry = max(ceiling(mtry.ratio * n_features), 1). Note that mtry and mtry.ratio are mutually exclusive.

Initial parameter values

num.threads is initialized to 1 to avoid conflicts with parallelization via future.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.ranger")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: mlr3, mlr3proba, mlr3extralearners, ranger

Parameters

Id	Type	Default	Levels	Range
alpha	numeric	0.5		$(-\infty, \infty)$
always.split.variables	untyped	-		-
holdout	logical	FALSE	TRUE, FALSE	-
importance	character	-	none, impurity, impurity_corrected, permutation	-
keep.inbag	logical	FALSE	TRUE, FALSE	-
max.depth	integer	NULL		$[0, \infty)$
min.node.size	integer	5		$[1, \infty)$
minprop	numeric	0.1		$(-\infty, \infty)$
mtry	integer	-		$[1, \infty)$
mtry.ratio	numeric	-		$[0, 1]$
num.random.splits	integer	1		$[1, \infty)$
num.threads	integer	1		$[1, \infty)$
num.trees	integer	500		$[1, \infty)$
oob.error	logical	TRUE	TRUE, FALSE	-
regularization.factor	untyped	1		-
regularization.usedepth	logical	FALSE	TRUE, FALSE	-
replace	logical	TRUE	TRUE, FALSE	-
respect.unordered.factors	character	ignore	ignore, order, partition	-
sample.fraction	numeric	-		$[0, 1]$
save.memory	logical	FALSE	TRUE, FALSE	-
scale.permutation.importance	logical	FALSE	TRUE, FALSE	-
seed	integer	NULL		$(-\infty, \infty)$
split.select.weights	numeric	-		$[0, 1]$
splitrule	character	logrank	logrank, extratrees, C, maxstat	-
verbose	logical	TRUE	TRUE, FALSE	-
write.forest	logical	TRUE	TRUE, FALSE	-
min.bucket	integer	3		$(-\infty, \infty)$
time.interest	integer	NULL		$[1, \infty)$
node.stats	logical	FALSE	TRUE, FALSE	-

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvRanger

Methods

Public methods

LearnerSurvRanger$new()
LearnerSurvRanger$importance()
LearnerSurvRanger$oob_error()
LearnerSurvRanger$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvRanger$new()

Method `importance()`

The importance scores are extracted from the model slot variable.importance.

Usage

LearnerSurvRanger$importance()

Returns

Named numeric().

Method `oob_error()`

The out-of-bag error is extracted from the model slot prediction.error.

Usage

LearnerSurvRanger$oob_error()

Returns

numeric(1).

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvRanger$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

be-marc

References

Wright, N. M, Ziegler, Andreas (2017). “ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R.” Journal of Statistical Software, 77(1), 1–17. doi:10.18637/jss.v077.i01.

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

Examples


# Define the Learner
learner = mlr3::lrn("surv.ranger", importance = "permutation")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.ranger", importance = "permutation")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Random Forest SRC Learner

Description

Random survival forest. Calls randomForestSRC::rfsrc() from randomForestSRC.

Prediction types

This learner returns two prediction types:

distr: a survival matrix in two dimensions, where observations are represented in rows and (unique event) time points in columns. Calculated using the internal randomForestSRC::predict.rfsrc() function.
crank: the expected mortality using mlr3proba::.surv_return().

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.rfsrc")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”
Feature Types: “logical”, “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, randomForestSRC, pracma

Parameters

Id	Type	Default	Levels	Range
ntree	integer	500		$[1, \infty)$
mtry	integer	-		$[1, \infty)$
mtry.ratio	numeric	-		$[0, 1]$
nodesize	integer	15		$[1, \infty)$
nodedepth	integer	-		$[1, \infty)$
splitrule	character	logrank	logrank, bs.gradient, logrankscore	-
nsplit	integer	10		$[0, \infty)$
importance	character	FALSE	FALSE, TRUE, none, permute, random, anti	-
block.size	integer	10		$[1, \infty)$
bootstrap	character	by.root	by.root, by.node, none, by.user	-
samptype	character	swor	swor, swr	-
samp	untyped	-		-
membership	logical	FALSE	TRUE, FALSE	-
sampsize	untyped	-		-
sampsize.ratio	numeric	-		$[0, 1]$
na.action	character	na.omit	na.omit, na.impute	-
nimpute	integer	1		$[1, \infty)$
ntime	integer	150		$[0, \infty)$
cause	integer	-		$[1, \infty)$
proximity	character	FALSE	FALSE, TRUE, inbag, oob, all	-
distance	character	FALSE	FALSE, TRUE, inbag, oob, all	-
forest.wt	character	FALSE	FALSE, TRUE, inbag, oob, all	-
xvar.wt	untyped	-		-
split.wt	untyped	-		-
forest	logical	TRUE	TRUE, FALSE	-
var.used	character	FALSE	FALSE, all.trees, by.tree	-
split.depth	character	FALSE	FALSE, all.trees, by.tree	-
seed	integer	-		$(-\infty, -1]$
do.trace	logical	FALSE	TRUE, FALSE	-
statistics	logical	FALSE	TRUE, FALSE	-
get.tree	untyped	-		-
outcome	character	train	train, test	-
ptn.count	integer	0		$[0, \infty)$
estimator	character	nelson	nelson, kaplan	-
cores	integer	1		$[1, \infty)$
save.memory	logical	FALSE	TRUE, FALSE	-
perf.type	character	-	none	-
case.depth	logical	FALSE	TRUE, FALSE	-

Custom mlr3 parameters

estimator: Hidden parameter that controls the type of estimator used to derive the survival function during prediction. The default value is "chf" which uses a bootstrapped Nelson-Aalen estimator for the cumulative hazard function $H(t)$ , (Ishwaran, 2008) from which we calculate $S(t) = \exp(-H(t))$ , whereas "surv" uses a bootstrapped Kaplan-Meier estimator to directly estimate $S(t)$ .

mtry: This hyperparameter can alternatively be set via the added hyperparameter mtry.ratio as mtry = max(ceiling(mtry.ratio * n_features), 1). Note that mtry and mtry.ratio are mutually exclusive.
sampsize: This hyperparameter can alternatively be set via the added hyperparameter sampsize.ratio as sampsize = max(ceiling(sampsize.ratio * n_obs), 1). Note that sampsize and sampsize.ratio are mutually exclusive.
cores: This value is set as the option rf.cores during training and is set to 1 by default.

Initial parameter values

ntime: Number of time points to coerce the observed event times for use in the estimated survival function during prediction. We changed the default value of 150 to 0 in order to be in line with other random survival forest learners and use all the unique event times from the train set.

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvRandomForestSRC

Methods

Public methods

LearnerSurvRandomForestSRC$new()
LearnerSurvRandomForestSRC$importance()
LearnerSurvRandomForestSRC$selected_features()
LearnerSurvRandomForestSRC$oob_error()
LearnerSurvRandomForestSRC$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvRandomForestSRC$new()

Method `importance()`

The importance scores are extracted from the model slot importance.

Usage

LearnerSurvRandomForestSRC$importance()

Returns

Named numeric().

Method `selected_features()`

Selected features are extracted from the model slot var.used.

Usage

LearnerSurvRandomForestSRC$selected_features()

Returns

character().

Method `oob_error()`

OOB error extracted from the model slot err.rate.

Usage

LearnerSurvRandomForestSRC$oob_error()

Returns

numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvRandomForestSRC$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS (2008). “Random survival forests.” The Annals of Applied Statistics, 2(3). doi:10.1214/08-aoas169, https://doi.org/10.1214/08-aoas169.

Breiman, Leo (2001). “Random Forests.” Machine Learning, 45(1), 5–32. ISSN 1573-0565, doi:10.1023/A:1010933404324.

Examples


# Define the Learner
learner = mlr3::lrn("surv.rfsrc", importance = "TRUE")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.rfsrc", importance = "TRUE")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Survival Support Vector Machine Learner

Description

Survival support vector machine. Calls survivalsvm::survivalsvm() from survivalsvm.

Details

Four possible SVMs can be implemented, dependent on the type parameter. These correspond to predicting the survival time via regression (regression), predicting a continuous rank (vanbelle1, vanbelle2), or a hybrid of the two (hybrid). Whichever type is chosen determines how the crank predict type is calculated, but in any case all can be considered a valid continuous ranking.

makediff3 is recommended when using type = "hybrid".

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.svm")

Meta Information

Task type: “surv”
Predict Types: “crank”, “response”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”
Required Packages: mlr3, mlr3proba, mlr3extralearners, survivalsvm

Parameters

Id	Type	Default	Levels	Range
type	character	regression	regression, vanbelle1, vanbelle2, hybrid	-
diff.meth	character	-	makediff1, makediff2, makediff3	-
gamma	numeric	NULL		$(-\infty, \infty)$
mu	numeric	NULL		$(-\infty, \infty)$
opt.meth	character	quadprog	quadprog, ipop	-
kernel	character	lin_kernel	lin_kernel, add_kernel, rbf_kernel, poly_kernel	-
kernel.pars	untyped	-		-
sgf.sv	integer	5		$[0, \infty)$
sigf	integer	7		$[0, \infty)$
maxiter	integer	20		$[0, \infty)$
margin	numeric	0.05		$[0, \infty)$
bound	numeric	10		$[0, \infty)$
eig.tol	numeric	1e-06		$[0, \infty)$
conv.tol	numeric	1e-07		$[0, \infty)$
posd.tol	numeric	1e-08		$[0, \infty)$

Prediction types

This learner returns up to two prediction types:

crank: a vector containing the continuous ranking scores, where each score corresponds to a specific test observation.
response: the survival time of each test observation, equal to -crank. This prediction type if only available for "type" equal to regression or hybrid.

Custom mlr3 parameters

gamma, mu have replaced gamma.mu so that it's easier to tune these separately. mu is only used when type = "hybrid".

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvSVM

Methods

Public methods

LearnerSurvSVM$new()
LearnerSurvSVM$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvSVM$new()

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvSVM$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Author(s)

RaphaelS1

References

Van Belle, Vanya, Pelckmans, Kristiaan, Van Huffel, Sabine, Suykens, AK J (2011). “Improved performance on high-dimensional survival data by application of Survival-SVM.” Bioinformatics, 27(1), 87–94.

Van Belle, Vanya, Pelckmans, Kristiaan, Van Huffel, Sabine, Suykens, AK J (2011). “Support vector methods for survival analysis: a comparison between ranking and regression approaches.” Artificial intelligence in medicine, 53(2), 107–118.

Shivaswamy, K P, Chu, Wei, Jansche, Martin (2007). “A support vector approach to censored targets.” In Seventh IEEE international conference on data mining (ICDM 2007), 655–660. IEEE.

Examples


set.seed(123)
# Define the Learner and set parameter values
learner = lrn("surv.svm", gamma = 0.1)
print(learner)

# Define a Task
task = mlr3::tsk("rats")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

set.seed(123)
# Define the Learner and set parameter values
learner = lrn("surv.svm", gamma = 0.1)
print(learner)

# Define a Task
task = mlr3::tsk("rats")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# print the model
print(learner$model)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Extreme Gradient Boosting AFT Survival Learner

Description

eXtreme Gradient Boosting regression using an Accelerated Failure Time objective. Calls xgboost::xgb.train() from package xgboost with objective set to survival:aft and eval_metric to aft-nloglik.

Prediction types

This learner returns three prediction types:

response: the estimated survival time $T$ for each test observation.
lp: a vector of linear predictors (relative risk scores), one per observation, estimated as $-log(T)$ . Higher survival time denotes lower risk.
crank: same as lp.

Initial parameter values

nrounds is initialized to 1000.
nthread is initialized to 1 to avoid conflicts with parallelization via future.
verbose is initialized to 0.

Early stopping

Early stopping can be used to find the optimal number of boosting rounds. The early_stopping_set parameter controls which set is used to monitor the performance. By default, early_stopping_set = "none" which disables early stopping. Set early_stopping_set = "test" to monitor the performance of the model on the test set while training. The test set for early stopping can be set with the "test" row role in the mlr3::Task. Additionally, the range must be set in which the performance must increase with early_stopping_rounds and the maximum number of boosting rounds with nrounds. While resampling, the test set is automatically applied from the mlr3::Resampling. Not that using the test set for early stopping can potentially bias the performance scores.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.xgboost.aft")

Meta Information

Task type: “surv”
Predict Types: “crank”, “lp”, “response”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, xgboost

Parameters

Id	Type	Default	Levels	Range
aft_loss_distribution	character	normal	normal, logistic, extreme	-
aft_loss_distribution_scale	numeric	-		$(-\infty, \infty)$
alpha	numeric	0		$[0, \infty)$
base_score	numeric	0.5		$(-\infty, \infty)$
booster	character	gbtree	gbtree, gblinear, dart	-
callbacks	untyped	list()		-
colsample_bylevel	numeric	1		$[0, 1]$
colsample_bynode	numeric	1		$[0, 1]$
colsample_bytree	numeric	1		$[0, 1]$
disable_default_eval_metric	logical	FALSE	TRUE, FALSE	-
early_stopping_rounds	integer	NULL		$[1, \infty)$
eta	numeric	0.3		$[0, 1]$
feature_selector	character	cyclic	cyclic, shuffle, random, greedy, thrifty	-
feval	untyped	NULL		-
gamma	numeric	0		$[0, \infty)$
grow_policy	character	depthwise	depthwise, lossguide	-
interaction_constraints	untyped	-		-
iterationrange	untyped	-		-
lambda	numeric	1		$[0, \infty)$
lambda_bias	numeric	0		$[0, \infty)$
max_bin	integer	256		$[2, \infty)$
max_delta_step	numeric	0		$[0, \infty)$
max_depth	integer	6		$[0, \infty)$
max_leaves	integer	0		$[0, \infty)$
maximize	logical	NULL	TRUE, FALSE	-
min_child_weight	numeric	1		$[0, \infty)$
missing	numeric	NA		$(-\infty, \infty)$
monotone_constraints	integer	0		$[-1, 1]$
normalize_type	character	tree	tree, forest	-
nrounds	integer	-		$[1, \infty)$
nthread	integer	1		$[1, \infty)$
ntreelimit	integer	-		$[1, \infty)$
num_parallel_tree	integer	1		$[1, \infty)$
one_drop	logical	FALSE	TRUE, FALSE	-
print_every_n	integer	1		$[1, \infty)$
process_type	character	default	default, update	-
rate_drop	numeric	0		$[0, 1]$
refresh_leaf	logical	TRUE	TRUE, FALSE	-
sampling_method	character	uniform	uniform, gradient_based	-
sample_type	character	uniform	uniform, weighted	-
save_name	untyped	-		-
save_period	integer	-		$[0, \infty)$
scale_pos_weight	numeric	1		$(-\infty, \infty)$
seed_per_iteration	logical	FALSE	TRUE, FALSE	-
skip_drop	numeric	0		$[0, 1]$
strict_shape	logical	FALSE	TRUE, FALSE	-
subsample	numeric	1		$[0, 1]$
top_k	integer	0		$[0, \infty)$
tree_method	character	auto	auto, exact, approx, hist, gpu_hist	-
tweedie_variance_power	numeric	1.5		$[1, 2]$
updater	untyped	-		-
verbose	integer	1		$[0, 2]$
watchlist	untyped	NULL		-
xgb_model	untyped	-		-
device	untyped	-		-

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvXgboostAFT

Active bindings

internal_valid_scores: The last observation of the validation scores for all metrics. Extracted from model$evaluation_log
internal_tuned_values: Returns the early stopped iterations if early_stopping_rounds was set during training.
validate: How to construct the internal validation data. This parameter can be either NULL, a ratio, "test", or "predefined".

Methods

Public methods

LearnerSurvXgboostAFT$new()
LearnerSurvXgboostAFT$importance()
LearnerSurvXgboostAFT$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvXgboostAFT$new()

Method `importance()`

The importance scores are calculated with xgboost::xgb.importance().

Usage

LearnerSurvXgboostAFT$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvXgboostAFT$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Note

To compute on GPUs, you first need to compile xgboost yourself and link against CUDA. See https://xgboost.readthedocs.io/en/stable/build.html#building-with-gpu-support.

Author(s)

bblodfon

References

Chen, Tianqi, Guestrin, Carlos (2016). “Xgboost: A scalable tree boosting system.” In Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 785–794. ACM. doi:10.1145/2939672.2939785.

Avinash B, Hyunsu C, Toby H (2022). “Survival Regression with Accelerated Failure Time Model in XGBoost.” Journal of Computational and Graphical Statistics. ISSN 15372715, doi:10.1080/10618600.2022.2067548.

Examples


# Define the Learner
learner = mlr3::lrn("surv.xgboost.aft")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.xgboost.aft")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Extreme Gradient Boosting Cox Survival Learner

Description

eXtreme Gradient Boosting regression using a Cox Proportional Hazards objective. Calls xgboost::xgb.train() from package xgboost with objective set to survival:cox and eval_metric to cox-nloglik.

Prediction types

Three types of prediction are returned for this learner:

lp: a vector of linear predictors (relative risk scores), one per observation.
crank: same as lp.
distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. By default, the Breslow estimator is used via mlr3proba::breslow().

Initial parameter values

nrounds is initialized to 1000.
nthread is initialized to 1 to avoid conflicts with parallelization via future.
verbose is initialized to 0.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.xgboost.cox")

Meta Information

Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, xgboost

Parameters

Id	Type	Default	Levels	Range
alpha	numeric	0		$[0, \infty)$
base_score	numeric	0.5		$(-\infty, \infty)$
booster	character	gbtree	gbtree, gblinear, dart	-
callbacks	untyped	list()		-
colsample_bylevel	numeric	1		$[0, 1]$
colsample_bynode	numeric	1		$[0, 1]$
colsample_bytree	numeric	1		$[0, 1]$
disable_default_eval_metric	logical	FALSE	TRUE, FALSE	-
early_stopping_rounds	integer	NULL		$[1, \infty)$
eta	numeric	0.3		$[0, 1]$
feature_selector	character	cyclic	cyclic, shuffle, random, greedy, thrifty	-
feval	untyped	NULL		-
gamma	numeric	0		$[0, \infty)$
grow_policy	character	depthwise	depthwise, lossguide	-
interaction_constraints	untyped	-		-
iterationrange	untyped	-		-
lambda	numeric	1		$[0, \infty)$
lambda_bias	numeric	0		$[0, \infty)$
max_bin	integer	256		$[2, \infty)$
max_delta_step	numeric	0		$[0, \infty)$
max_depth	integer	6		$[0, \infty)$
max_leaves	integer	0		$[0, \infty)$
maximize	logical	NULL	TRUE, FALSE	-
min_child_weight	numeric	1		$[0, \infty)$
missing	numeric	NA		$(-\infty, \infty)$
monotone_constraints	integer	0		$[-1, 1]$
normalize_type	character	tree	tree, forest	-
nrounds	integer	-		$[1, \infty)$
nthread	integer	1		$[1, \infty)$
num_parallel_tree	integer	1		$[1, \infty)$
one_drop	logical	FALSE	TRUE, FALSE	-
print_every_n	integer	1		$[1, \infty)$
process_type	character	default	default, update	-
rate_drop	numeric	0		$[0, 1]$
refresh_leaf	logical	TRUE	TRUE, FALSE	-
sampling_method	character	uniform	uniform, gradient_based	-
sample_type	character	uniform	uniform, weighted	-
save_name	untyped	-		-
save_period	integer	-		$[0, \infty)$
scale_pos_weight	numeric	1		$(-\infty, \infty)$
seed_per_iteration	logical	FALSE	TRUE, FALSE	-
skip_drop	numeric	0		$[0, 1]$
strict_shape	logical	FALSE	TRUE, FALSE	-
subsample	numeric	1		$[0, 1]$
top_k	integer	0		$[0, \infty)$
tree_method	character	auto	auto, exact, approx, hist, gpu_hist	-
tweedie_variance_power	numeric	1.5		$[1, 2]$
updater	untyped	-		-
verbose	integer	1		$[0, 2]$
watchlist	untyped	NULL		-
xgb_model	untyped	-		-
device	untyped	-		-

Early stopping

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvXgboostCox

Active bindings

internal_valid_scores: The last observation of the validation scores for all metrics. Extracted from model$evaluation_log
internal_tuned_values: Returns the early stopped iterations if early_stopping_rounds was set during training.
validate: How to construct the internal validation data. This parameter can be either NULL, a ratio, "test", or "predefined".

Methods

Public methods

LearnerSurvXgboostCox$new()
LearnerSurvXgboostCox$importance()
LearnerSurvXgboostCox$clone()

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

LearnerSurvXgboostCox$new()

Method `importance()`

The importance scores are calculated with xgboost::xgb.importance().

Usage

LearnerSurvXgboostCox$importance()

Returns

Named numeric().

Method `clone()`

The objects of this class are cloneable with this method.

Usage

LearnerSurvXgboostCox$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Note

To compute on GPUs, you first need to compile xgboost yourself and link against CUDA. See https://xgboost.readthedocs.io/en/stable/build.html#building-with-gpu-support.

Author(s)

bblodfon

References

Examples


# Define the Learner
learner = mlr3::lrn("surv.xgboost.cox")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

# Define the Learner
learner = mlr3::lrn("surv.xgboost.cox")
print(learner)

# Define a Task
task = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
print(learner$importance())

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()

Package 'mlr3extralearners'

Help Index

mlr3extralearners: Extra Learners For mlr3

Description

Author(s)

Create a New Learner

Description

Usage

Arguments

Examples

Install Learner Dependencies

Description

Usage

Arguments

List Learners in mlr3verse

Description

Usage

Arguments

Classification Abess Learner

Description

Dictionary

Meta Information

Parameters

Initial parameter values

Super classes

Methods

Public methods

Method new()

Usage

Method selected_features()

Usage

Returns

Method clone()

Usage

Arguments

Author(s)

See Also

Examples

Classification AdaBoostM1 Learner

Description

Dictionary

Meta Information

Parameters

Parameter changes

Super classes

Methods

Public methods

Method new()

Usage

Method clone()

Usage

Arguments

Author(s)

References

See Also

Examples

Classification BART (Bayesian Additive Regression Trees) Learner

Description

Dictionary

Meta Information

Parameters

Parameter Changes

Super classes

Methods

Public methods

Method new()

Usage

Method clone()

Usage

Arguments

Author(s)

References

See Also

Examples

Classification Bayes Network Learner

Description

Custom mlr3 parameters

Dictionary

Meta Information

Parameters

Method `new()`

Method `selected_features()`

Method `clone()`

Method `new()`

Method `clone()`

Method `new()`

Method `clone()`

Method `new()`

Method `clone()`

Method `new()`

Method `clone()`

Method `new()`

Method `importance()`

Method `clone()`

Method `new()`

Method `oob_error()`

Method `clone()`