Feature selection algorithms.
These include univariate filter selection methods and the recursive feature elimination algorithm.
Univariate feature selector with configurable strategy.
Feature ranking with recursive feature elimination.
Recursive feature elimination with cross-validation to select features.
Filter: Select the p-values for an estimated false discovery rate.
Filter: Select the pvalues below alpha based on a FPR test.
Meta-transformer for selecting features based on importance weights.
Filter: Select the p-values corresponding to Family-wise error rate.
Select features according to the k highest scores.
Select features according to a percentile of the highest scores.
Transformer mixin that performs feature selection given a support mask
Transformer that performs Sequential Feature Selection.
Feature selector that removes all low-variance features.
Compute chi-squared stats between each non-negative feature and class.
Compute the ANOVA F-value for the provided sample.
Univariate linear regression tests returning F-statistic and p-values.
Estimate mutual information for a discrete target variable.
Estimate mutual information for a continuous target variable.
Compute Pearson's r for each features and the target.
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