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. and Num. show if a particular method can deal with factor and numeric features.

Method Package Description Classif Regr Surv Fac. Num.
anova.test 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
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 Kurskal Test for binary and multiclass classification tasks X X X
linear.correlation FSelector Pearson correlation between feature and target X X
mrmr mRMRe Minimum redundancy, maximum relevance filter X X X X X
oneR FSelector oneR assocation rule X X X X
permutation.importance the aggregate difference between feature permuted and unpermuted predictions X X X X X
rank.correlation FSelector Spearman's correlation between feature and target X X X
relief FSelector RELIEF algorithm X X X X
rf.importance randomForestSRC Importance of random forests X X X X X
rf.min.depth randomForestSRC Minimal depth of random forest fitted in package 'randomForestSRC' X X X X X
symmetrical.uncertainty FSelector Entropy-based symmetrical uncertainty between feature and target X X X X
univariate Construct a simple performance filter using a mlr learner X X X X X
variance A simple variance filter X X X X