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.