aorsf learnerresample() or benchmark() (#353)$model slot of lrn("classif.abess") now contains the model of
the upstream package again.lrn("surv.xgboost.aft")
and lrn("surv.xgboost.cox").case.depth parameter to rfsrc learners.mlr3 is now in Depends instead of Imports.lrn("surv.xgboost") was now removed.
Use lrn("surv.xgboost.cox") or lrn("surv.xgboost.aft") instead.surv.xgboost.cox and surv.xgboost.aft separate survival learners. distr prediction on the cox xgboost learner is now estimated via Breslow by default and aft xgboost has now in addition a response prediction (survival time)surv.parametric code to survivalmodels, changed type parameter to form to avoid conflict with survivalmodels's default parameter listVectorDistributions from partykit and flexsurv survival learners with survival matrices (Matdist) (thanks to @bblodfon)discrete parameter in surv.parametric learner to return Matdist survival predictionsselected_features() to CoxBoost survival learners (thanks to @bblodfon)surv.ranger now receives parameters during $predict() (thanks to @jemus42)surv.bart was added (thanks to @bblodfon)lrn("surv.aorsf") were updated (thanks to @bcjaeger)distr predict type to the surv.cv_glmnet and surv.glmnet
learners (thanks to @bblodfon)I and F params from IBk learner are too interdependent (I can only be TRUE when F is FALSE and vice versa).
Combined them into one factor param weight that has two levels -- I and F.U must be FALSE for S to be tunable in J48 learner.perf.type to rfsrc learners"multiclass" property from lrn("classif.gbm"), as this feature is broken."factor" to gam learnersmin.bucket to rangernthreads to dbarts learners; set verbose to FALSE by default (thanks to @ck37)regr.abess and classif.abess (thanks to @bbayukari)LearnerClassifImbalancedRandomForestSRC (thanks to
@HarutyunyanLiana)LearnerClassifPriorityLasso, LearnerRegrPriorityLasso, LearnerSurvPriorityLasso (thanks to
@HarutyunyanLiana)LearnerClassifGlmer (https://github.com/mlr-org/mlr3extralearners/issues/243)nei and ncv.thread that were added to mgcv::gam() in
version 1.8.41"weights" to LearnerClassifGlmer and
LearnerRegrLmerlightgbm uses the param_vals stored in the state for hotstartingstate$data_prototype to get ordering of features via
ordered_features() like in mlr3learners and therefore obviate the need to
store feature_names in the statemlr_learners when unloading
mlr3extralearners"integer" to classif.randomForest"logical" to {classif, regr}.randomForestlist_mlr3learners() function. Now slower but correct.keep_data instead of keep.dataLearnerSurvAorsf with key surv.aorsf. See https://github.com/bcjaeger/aorsf for more details on aorsfcreate_learner and the learner template.Fix bug in C50 learner: Weights were not passed correctly
Remove kerdiest Learner because it is not being maintained on CRAN anymore
Fix bugs in learners lmer and J48
Remove predict type proba from J48
Delay loading of mlr3proba learners
lightgbm:
Docs: Renamed section "Custom mlr3 defaults" to "Parameter Changes"
Added labels to learners
Remove extraTrees because it is no longer on CRAN and GH version has errors
Remove sketch_eps parameter from xgboost because it is no longer listed in the docs
regr.lmerchore: add new parameters for kde and rfsrc
temporarily disable feat_all test for obliqeRSF (failed in $score() stage, because issue only happened in CI and could not be reproduced
early_stopping_split for lightgbm learnersdens.plugcreate_learnercategorical_features in {lightgbm} learnerslightgbm updatescreate_learnerignored_features and one_hot_max_size to classif.catboostregr.rvm and classif.lssvmrandomForestSRC::rfsrc(),
partykit::cforest() and obliqueRSF::ORSF() to conveniently tune
hyperparameters whose upper limit depends on data dimensions.regr.gaussprregr.gausspr and classif.gausspr from kernlab::gaussprinstall_catboost to make installation from catboost simplerbase parameter of {bart} learnersLearnerRegrCubist and LearnerRegrMarsnnet learners to mlr3learnersrfsrc learners to 1LearnerRegrGam and LearnerClassifGam with keys regr.gam and classif.gam from package mgcv.surv.coxboost now uses the GitHub version instead of CRAN (archived)regr.glmboostsurv.svm now supports all feature typesLearnerRegrLightGBM and LearnerClassifLightGBM with keys regr.lightgbm and classif.lightgbm respectively. Copied from mlr3learners.lightgbmLearnerRegrLiblineaRX and LearnerClassifLiblineaRX deprecated in favour of only two learners (LearnerRegrLiblineaR and LearnerClassLiblineaR) with added hyper-parameters. Deprecated learners will be removed in v0.3.0.classif.nnet will be removed in v0.4.0.liblinearX will be removed in v0.4.0.dist = "logistic" has been removed from surv.parametric as it is unclear what this was previously predicting.type = "tobit" for dist = "gaussian" so predictions can correspond with survival::survreg.LearnerRegrGlm with the unique key regr.glm from package stats, which allows users to change the family hyperparameter when fitting generalized linear regression models.keeptrees parameter from classif.bart as this is forced internallyclassif.bartclassif.earth and regr.earthse predict type to regr.earthregr.knn and classif.knnmlr3proba moved to Suggestsinstall_learners now additionally installs required mlr3 packagessurv.parametric occurring if feature names are switched between training and predictingclassif.nnet, in the future please load from mlr3learnerscrank and distr computation of all survival learnerssurv learners that were reversing the order of crank, see this issue for full details: https://github.com/mlr-org/mlr3proba/issues/165response is no longer returned by surv.mboost, surv.blackboost, surv.glmboost, surv.gamboost or surv.parametricsurv.parametric with ph formsurvivalmodelslearners which weren't returning distrsurv.coxboost and surv.coxboost_cv can now only handle integer and numeric feature types, previous automated internal coercions were inconsistent with mlr3 design.