Feature filter “univariate” had a bad name, was deprecated and is now called “univariate.model.score”. The new one also has better defaults.
(generate/plot)PartialPrediction: added new arg “geom” for tile plots
small fix for plotBMRSummary
the ModelMultiplexer inherits its predict.type from the base learners now
check that learners in an ensemble have the same predict.type
new function getBMRModels to extract stored models from a benchmark result
Fixed a bug where several learners from the LiblineaR package (“classif.LiblineaRL2LogReg”, “classif.LiblineaRL2SVC”, “regr.LiblineaRL2L2SVR”) were calling the wrong value for “type” (0) and thus training the wrong model.
Fixed a bug where the resampling objects hout, cv2, cv3, cv5, cv10 were not documented in the ResampleDesc help page
regr.xgboost, classif.xgboost: add feval param
fixed a bug in irace tuning interface with unamed discrete values
Fixed bugs in “jackknife” and “bootstrap” se estimators for regr.randomForest.
Added “sd” estimator for regr.randomForest.
Fixed a mini bug in ModelMultiplexer where hyperpars that are only needed in predict were not passed down correctly
Fixed a bug where the function capLargeValues wasn’t working if you passed a task.
capLargeValues now has a new argument “target”, to prevent from capping response values.
classif.gbm, regr.gbm: Updated possible ‘distribution’ settings a bit.
oversample, undersample, makeOversampleWrapper, makeUndersampleWrapper, makeOverBaggingWrapper: Added arguments to specifically select the sampled class.
listLearners now returns a data frame with properties of the learners if create is false