mlr3learners - mlr3learners 0.12.0

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

- R
Published by be-marc 9 months ago

mlr3learners - 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

- R
Published by be-marc 10 months ago

mlr3learners - 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.

- R
Published by be-marc 11 months ago

mlr3learners - 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.

- R
Published by be-marc about 1 year ago

mlr3learners - 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.

- R
Published by be-marc over 1 year ago

mlr3learners - mlr3learners 0.7.0

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

- R
Published by be-marc over 1 year ago

mlr3learners - mlr3learners 0.6.0

• Adaption to new paradox version 1.0.0.

- R
Published by be-marc almost 2 years ago

mlr3learners - mlr3learners 0.5.8

• Adaption to memory optimization in mlr3 0.17.1.

- R
Published by be-marc about 2 years ago

mlr3learners - 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.

- R
Published by be-marc over 2 years ago

mlr3learners - mlr3learners 0.5.6

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

- R
Published by mllg about 3 years ago

mlr3learners - 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.

- R
Published by mllg over 3 years ago

mlr3learners - 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.

- R
Published by mllg about 4 years ago

mlr3learners - 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.

- R
Published by mllg over 4 years ago

mlr3learners - 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 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).

- R
Published by mllg over 4 years ago

mlr3learners - mlr3learners 0.4.5

• Fixed SVM learners for new release of package e1071.

- R
Published by mllg almost 5 years ago

mlr3learners - 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).

- R
Published by mllg almost 5 years ago

mlr3learners - mlr3learners 0.4.3

- R
Published by mllg about 5 years ago

mlr3learners - mlr3learners 0.4.2

Fixed a bug in the survival random forest LearnerSurvRanger.

- R
Published by mllg over 5 years ago

mlr3learners - mlr3learners 0.4.1

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

- R
Published by mllg over 5 years ago

mlr3learners - 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.

- R
Published by mllg over 5 years ago

mlr3learners - 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).
• There is now a vignette "Additional Learners" listing all external learners which live in the mlr3learners organization. See mlr3learners.drat for easy installation.

- R
Published by pat-s almost 6 years ago

mlr3learners - 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

- R
Published by mllg over 6 years ago

mlr3learners - mlr3learners 0.1.4

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

- R
Published by mllg over 6 years ago

mlr3learners - mlr3learners 0.1.3

• Changes to work with new mlr3::Learner API.
• Improved documentation.
• Added references.

- R
Published by mllg over 6 years ago