mlr3learners 0.14.0

• compatibility: xgboost 3.1.2.1

mlr3learners 0.13.0

• 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.

mlr3learners 0.12.0

• feat: Add classif.kknn and regr.kknn learners.

mlr3learners 0.11.0

• 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

mlr3learners 0.10.0

• 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.

mlr3learners 0.9.0

• 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.

mlr3learners 0.8.0

• 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.

mlr3learners 0.7.0

• feat: LearnerClassifXgboost and LearnerRegrXgboost now support internal tuning and validation. This now also works in conjunction with mlr3pipelines.

mlr3learners 0.6.0

• Adaption to new paradox version 1.0.0.

mlr3learners 0.5.8

• Adaption to memory optimization in mlr3 0.17.1.

mlr3learners 0.5.7

• 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.

mlr3learners 0.5.6

• Enable new early stopping mechanism for xgboost.
• Improved documentation.
• fix: unloading mlr3learners removes learners from dictionary.

mlr3learners 0.5.4

• 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.

mlr3learners 0.5.3

• Survival learners have been moved to mlr3extralearners (maintained on Github): https://github.com/mlr-org/mlr3extralearners

mlr3learners 0.5.2

• 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.

mlr3learners 0.5.1

• 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.

mlr3learners 0.5.0

• 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).

mlr3learners 0.4.5

• Fixed SVM learners for new release of package e1071.

mlr3learners 0.4.4

• 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).

mlr3learners 0.4.3

• 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).

mlr3learners 0.4.2

• Fixed a bug in the survival random forest LearnerSurvRanger.

mlr3learners 0.4.1

• Disabled some glmnet tests on solaris.
• Removed dependency on orphaned package bibtex.

mlr3learners 0.4.0

• 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.

mlr3learners 0.3.0

• 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)

mlr3learners 0.2.0

• 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).

mlr3learners 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)

mlr3learners 0.1.6

• Added missing feature type logical() to multiple learners.

mlr3learners 0.1.5

• 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

mlr3learners 0.1.4

• Fixed a bug in *.xgboost and *.svm which was triggered if columns were reordered between $train() and $predict().

mlr3learners 0.1.3

• 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

mlr3learners 0.1.2

• Maintenance release.

mlr3learners 0.1.1

• Initial upload to CRAN.