ensemble_standard_deviation and law_of_total_variance to regr.ranger learner.nrounds for xgboost learners.classif.kknn and regr.kknn learners.kknn package was removed from CRAN.
The classif.kknn and regr.kknn learners are now removed from mlr3learners.xgboost, glmnet, lm and glm learners.$selected_features() method to classif.ranger and regr.ranger learners.$loglik() method from all learners.lrn("classif.ranger") and lrn("regr.ranger") for 0.17.0, adding na.action parameter and "missings" property, and poisson splitrule for regression with a new poisson.tau parameter.lrn("classif.ranger") and lrn("regr.ranger").
Remove alpha and minprop hyperparameter.
Remove default of respect.unordered.factors.
Change lower bound of max_depth from 0 to 1.
Remove se.method from lrn("classif.ranger").base_margin in xgboost learners (#205).lrn("regr.xgboost") now works properly. Previously the training data was used.eval_metric must now be set.
This achieves that one needs to make the conscious decision which performance metric to use for early stopping.LearnerClassifXgboost and LearnerRegrXgboost now support internal tuning and validation.
This now also works in conjunction with mlr3pipelines.nnet learner and support feature type "integer".min.bucket parameter to classif.ranger and regr.ranger.mlr3learners removes learners from dictionary.regr.nnet learner.classif.log_reg.default_values() function for ranger and svm learners.eval_metric() is now explicitly set for xgboost learners to silence a
deprecation warning.mtry.ratio is converted to mtry to
simplify tuning.glm and glmnet (#199). While predictions in previous versions
were correct, the estimated coefficients had the wrong sign.lambda and s for glmnet learners (#197).glmnet now support to extract selected features (#200).kknn now raise an exception if k >= n (#191).ranger now come with the virtual hyperparameter
mtry.ratio to set the hyperparameter mtry based on the proportion of
features to use.$loglik()), allowing to calculate measures like AIC or BIC in mlr3 (#182).e1071.set_threads() in mlr3 provides a generic way to set the
respective hyperparameter to the desired number of parallel threads.survival:aft objective to surv.xgboostpredict.all from ranger learners (#172).surv.ranger, c.f. https://github.com/mlr-org/mlr3proba/issues/165.classif.nnet learner (moved from mlr3extralearners).LearnerSurvRanger.glmnet tests on solaris.bibtex.classif.glmnet and classif.cv_glmnet
with predict_type set to "prob" (#155).glmnet to be more robust if the order of
features has changed between train and predict.$model slot of the {kknn} learner now returns a list containing some
information which is being used during the predict step.
Before, the slot was empty because there is no training step for kknn.saveRDS(), serialize() etc.penalty.factor is a vector param, not a ParamDbl (#141)mxitnr and epsnr from glmnet v4.0 updatesurv.glmnet (#130)mlr3proba (#144)surv.xgboost (#135)surv.ranger (#134)cv_glmnet and glmnet (#99)predict.gamma and newoffset arg (#98)inst/paramtest was added.
This test checks against the arguments of the upstream train & predict
functions and ensures that all parameters are implemented in the respective
mlr3 learner (#96).interaction_constraints to {xgboost} learners (#97).classif.multinom from package nnet.regr.lm and classif.log_reg now ignore the global option
"contrasts".additional-learners.Rmd listing all mlr3 custom learnersinteraction_constraints (#95)logical() to multiple learners.regr.glmnet, regr.km,
regr.ranger, regr.svm, regr.xgboost, classif.glmnet, classif.lda,
classif.naivebayes, classif.qda, classif.ranger and classif.svm.glmnet: Added relax parameter (v3.0)xgboost: Updated parameters for v0.90.0.2*.xgboost and *.svm which was triggered if columns
were reordered between $train() and $predict().Changes to work with new mlr3::Learner API.
Improved documentation.
Added references.
add new parameters of xgboost version 0.90.2
add parameter dependencies for xgboost