Integrated Filter Methods

The following table shows the available methods for calculating the feature importance. Columns Classif, Regr and Surv indicate if classification, regression or survival analysis problems are supported. Columns Fac., Num. and Ord. show if a particular method can deal with factor, numeric and ordered factor features.

Current methods

Method Package Description Classif Regr Surv Fac. Num. Ord.
anova.test Rfast ANOVA Test for binary and multiclass classification tasks X X
carscore care CAR scores X X
cforest.importance party Permutation importance of random forest fitted in package 'party' X X X X X X
chi.squared FSelector Chi-squared statistic of independence between feature and target X X X X
gain.ratio FSelector Entropy-based gain ratio between feature and target X X X X
information.gain FSelector Entropy-based information gain between feature and target X X X X
kruskal.test Kruskal Test for binary and multiclass classification tasks X X X
linear.correlation Rfast Pearson correlation between feature and target X X
mrmr mRMRe Minimum redundancy, maximum relevance filter X X X X
oneR FSelector oneR association rule X X X X
permutation.importance Aggregated difference between feature permuted and unpermuted predictions X X X X X X
randomForest.importance randomForest Importance based on OOB-accuracy or node inpurity of random forest fitted in package 'randomForest'. X X X X
randomForestSRC.rfsrc randomForestSRC Importance of random forests fitted in package 'randomForestSRC'. Importance is calculated using argument 'permute'. X X X X X X
randomForestSRC.var.select randomForestSRC Minimal depth of / variable hunting via method var.select on random forests fitted in package 'randomForestSRC'. X X X X X X
rank.correlation Rfast Spearman's correlation between feature and target X X
relief FSelector RELIEF algorithm X X X X
symmetrical.uncertainty FSelector Entropy-based symmetrical uncertainty between feature and target X X X X
univariate.model.score Resamples an mlr learner for each input feature individually. The resampling performance is used as filter score, with rpart as default learner. X X X X X X
variance A simple variance filter X X X X

Deprecated methods

Method Package Description Classif Regr Surv Fac. Num. Ord.
rf.importance randomForestSRC Importance of random forests fitted in package 'randomForestSRC'. Importance is calculated using argument 'permute'. (DEPRECATED) X X X X X X
rf.min.depth randomForestSRC Minimal depth of random forest fitted in package 'randomForestSRC. (DEPRECATED) X X X X X X
univariate Resamples an mlr learner for each input feature individually. The resampling performance is used as filter score, with rpart as default learner. (DEPRECATED) X X X X X X