resample now returns an object of class ResampleResult (downward compatible) to allow for a print method.
resampling on features now supported for an arbitrary number of factor features
mlr supports ViperCharts plots now
ROC plot via ROCR can now be created automatically, before you had to call asROCRPrediction, then construct the plots via ROCR your self. See plotROCRCurves
all mlr measures now have slots “name” and “note”
exported a few very simple “getters” for tasks, see below
in makeLearner a probability predict.threshold can be set for classifiers, also see setPredictThreshold
in the control objects for tuning and feature selection, the user can now enable threshold tuning
in the control objects for tuning and feature selection, the user can now define his own logging function
default console logging for tuneParams and selectFeatures is more informative, it displays time and memory info
updated some properties of some learners
Default arguments of classif.bartMachine, classif.randomForestSRC, regr.randomForestSRC and sur.randomForestSRC have been changed to allow missing data support with default settings.
externalized measure functions to be used on vectors.
some minor bug fixes
required basic learner packages are not loaded into the global namespace anymore, requireNamespace is used internally instead. this ensures less name clashes and name shadowing
resample passes dot arguments to the learner hyperpars
new option “on.par.out.of.bounds” to disable out-of-bound checks for model parameters
measures were slightly internally changed. they expose more properties (check ?Measure) and some now unnecessary object slots were removed
classif.lda and classif.qda now have hyperpar “predict.method”
filterFeatures and makeFilterWrapper gain an argument for mandatory features
plotLearnerPrediction has new option “err.size”
classif.plsDA and cluster.DBscan for now removed because of problems with the underlying learning algorithm
new aggregation test.join
the following models now can handle factors and ordereds by extra dummy or int encoding: classif.glmnet, regr.glmnet, surv.glmnet, surv.cvglmnet, surv.penalized, surv.optimCoxBoostPenalty, surv.glmboost, surv.CoxBoost