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mlr3learners (development version)

  • 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

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. mlr-org/mlr3proba#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.