Changes in version 0.15.0 (2026-06-09) - compatibility: glmnet 5.0. - feat: All learners now support the predict_raw flag to store raw prediction objects from upstream models. - feat: classif.log_reg gains a link hyperparameter to support probit regression (#376). - feat: classif.ranger and regr.ranger gain a native_model field to access the underlying ranger object (#375). - feat: Add parameter local.importance to ranger learners (#377). - fix: glmnet_selected_features() now works correctly with glmnet >= 5.0. - fix: Using offset during prediction for xgboost learners (#371). - fix: Importance in doc examples is now printed correctly. - fix: xgboost parameter set (#384). - refactor: Went through glmnet parameters and updated their order, defaults, removed those that are not utilized by corresponding learners. - fix: Use of relax = TRUE with glmnet learners. - feat: cv_glmnet learners gained a seed parameter to make the random fold assignment of cv.glmnet, and therefore the selected lambda, reproducible. Changes in version 0.14.0 (2025-12-13) - compatibility: xgboost 3.1.2.1 Changes in version 0.13.0 (2025-10-02) - feat: Add new uncertainty estimation methods ensemble_standard_deviation and law_of_total_variance to regr.ranger learner. - fix: Default nrounds for xgboost learners. - feat: Store ranger oob error without storing models. - fix: Only allow simple measures as internal measures for xgboost learners. Changes in version 0.12.0 (2025-05-23) - feat: Add classif.kknn and regr.kknn learners. Changes in version 0.11.0 (2025-05-17) - BREAKING CHANGE: The kknn package was removed from CRAN. The classif.kknn and regr.kknn learners are now removed from mlr3learners. - compatibility: mlr3 1.0.0 Changes in version 0.10.0 (2025-03-19) - feat: Support offset during training and prediction in xgboost, glmnet, lm and glm learners. - feat: Add $selected_features() method to classif.ranger and regr.ranger learners. Changes in version 0.9.0 (2024-11-23) - BREAKING CHANGE: Remove $loglik() method from all learners. - feat: Update hyperparameter set of 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. - compatibility: mlr3 0.22.0. Changes in version 0.8.0 (2024-10-25) - fix: Hyperparameter set of 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"). - feat: use base_margin in xgboost learners (#205). - fix: validation for learner lrn("regr.xgboost") now works properly. Previously the training data was used. - feat: add weights for logistic regression again, which were incorrectly removed in a previous release (#265). - BREAKING CHANGE: When using internal tuning for xgboost learners, the eval_metric must now be set. This achieves that one needs to make the conscious decision which performance metric to use for early stopping. - BREAKING CHANGE: Change xgboost default nrounds from 1 to 1000. Changes in version 0.7.0 (2024-06-28) - feat: LearnerClassifXgboost and LearnerRegrXgboost now support internal tuning and validation. This now also works in conjunction with mlr3pipelines. Changes in version 0.6.0 (2024-03-13) - Adaption to new paradox version 1.0.0. Changes in version 0.5.8 (2023-12-21) - Adaption to memory optimization in mlr3 0.17.1. Changes in version 0.5.7 (2023-11-21) - Added labels to learners. - Added formula argument to nnet learner and support feature type "integer". - Added min.bucket parameter to classif.ranger and regr.ranger. Changes in version 0.5.6 (2023-01-06) - Enable new early stopping mechanism for xgboost. - Improved documentation. - fix: unloading mlr3learners removes learners from dictionary. Changes in version 0.5.4 (2022-08-15) - Added regr.nnet learner. - Removed the option to use weights in classif.log_reg. - Added default_values() function for ranger and svm learners. - Improved documentation. Changes in version 0.5.3 (2022-05-25) - Survival learners have been moved to mlr3extralearners (maintained on Github): https://github.com/mlr-org/mlr3extralearners Changes in version 0.5.2 (2022-01-22) - Most learners now reorder the columns in the predict task according to the order of columns in the training task. - Removed workaround for old mlr3 versions. Changes in version 0.5.1 (2021-11-19) - eval_metric() is now explicitly set for xgboost learners to silence a deprecation warning. - Improved how the added hyperparameter mtry.ratio is converted to mtry to simplify tuning. - Multiple updates to hyperparameter sets. Changes in version 0.5.0 (2021-08-17) - Fixed the internal encoding of the positive class for classification learners based on glm and glmnet (#199). While predictions in previous versions were correct, the estimated coefficients had the wrong sign. - Reworked handling of lambda and s for glmnet learners (#197). - Learners based on glmnet now support to extract selected features (#200). - Learners based on kknn now raise an exception if k >= n (#191). - Learners based on ranger now come with the virtual hyperparameter mtry.ratio to set the hyperparameter mtry based on the proportion of features to use. - Multiple learners now support the extraction of the log-likelihood (via method $loglik()), allowing to calculate measures like AIC or BIC in mlr3 (#182). Changes in version 0.4.5 (2021-03-18) - Fixed SVM learners for new release of package e1071. Changes in version 0.4.4 (2021-03-15) - Changed hyperparameters of all learners to make them run sequentially in their defaults. The new function set_threads() in mlr3 provides a generic way to set the respective hyperparameter to the desired number of parallel threads. - Added survival:aft objective to surv.xgboost - Removed hyperparameter predict.all from ranger learners (#172). Changes in version 0.4.3 (2020-12-08) - Fixed stochastic test failures on solaris. - Fixed surv.ranger, c.f. https://github.com/mlr-org/mlr3proba/issues/165. - Added classif.nnet learner (moved from mlr3extralearners). Changes in version 0.4.2 (2020-11-11) - Fixed a bug in the survival random forest LearnerSurvRanger. Changes in version 0.4.1 (2020-10-07) - Disabled some glmnet tests on solaris. - Removed dependency on orphaned package bibtex. Changes in version 0.4.0 (2020-09-25) - Fixed a potential label switch in classif.glmnet and classif.cv_glmnet with predict_type set to "prob" (#155). - Fixed learners from package glmnet to be more robust if the order of features has changed between train and predict. Changes in version 0.3.0 (2020-08-29) - The $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. - Compact in-memory representation of R6 objects to save space when saving mlr3 objects via saveRDS(), serialize() etc. - glmnet learners: penalty.factor is a vector param, not a ParamDbl (#141) - glmnet: Add params mxitnr and epsnr from glmnet v4.0 update - Add learner surv.glmnet (#130) - Suggest package mlr3proba (#144) - Add learner surv.xgboost (#135) - Add learner surv.ranger (#134) Changes in version 0.2.0 (2020-04-22) - Split glmnet learner into cv_glmnet and glmnet (#99) - glmnet learners: Add predict.gamma and newoffset arg (#98) - We now test that all learners can be constructed without parameters. - A new custom "Paramtest" which lives 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). - A lot missing parameters were added to learners. See #96 for a complete list. - Add parameter interaction_constraints to {xgboost} learners (#97). Changes in version 0.1.6.9000 - Added learner classif.multinom from package nnet. - Learners regr.lm and classif.log_reg now ignore the global option "contrasts". - Add vignette additional-learners.Rmd listing all mlr3 custom learners - Move Learner*Glmnet to Learner*CVGlmnet and add Learner*Glmnet (without internal tuning) (#90) XGBoost - Add parameter interaction_constraints (#95) Changes in version 0.1.6 (2020-02-10) - Added missing feature type logical() to multiple learners. Changes in version 0.1.5 (2019-11-25) - Added parameter and parameter dependencies to 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 Changes in version 0.1.4 (2019-10-29) - Fixed a bug in *.xgboost and *.svm which was triggered if columns were reordered between $train() and $predict(). Changes in version 0.1.3 (2019-09-17) - 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 Changes in version 0.1.2 (2019-08-26) - Maintenance release. Changes in version 0.1.1 (2019-08-05) - Initial upload to CRAN.