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.
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 | ||
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 |
symmetrical.uncertainty | FSelector | Entropy-based symmetrical uncertainty between feature and target | 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 | |
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 |