Mlr3learners Versions Save

Recommended learners for mlr3

v0.6.0

2 months ago
  • Adaption to new paradox version 1.0.0.

v0.5.8

4 months ago
  • Adaption to memory optimization in mlr3 0.17.1.

v0.5.7

5 months ago
  • 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.

v0.5.6

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

v0.5.4

1 year ago
  • 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.

v0.5.2

2 years ago
  • 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.

v0.5.1

2 years ago
  • 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.

v0.5.0

2 years ago
  • 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 a 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).

v0.4.5

3 years ago
  • Fixed SVM learners for new release of package e1071.

v0.4.4

3 years ago
  • 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).