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.
ID | 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 | |
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 |