sklearn.feature_selection

feature selection algorithms.

These include univariate filter selection methods and the recursive feature elimination algorithm.

User guide. See the feature selection section for further details.

GenericUnivariateSelect	Univariate feature selector with configurable strategy.
RfE	feature ranking with recursive feature elimination.
RfECV	Recursive feature elimination with cross-validation to select features.
Selectfdr	filter: Select the p-values for an estimated false discovery rate.
Selectfpr	filter: Select the pvalues below alpha based on a fPR test.
SelectfromModel	Meta-transformer for selecting features based on importance weights.
Selectfwe	filter: Select the p-values corresponding to family-wise error rate.
SelectKBest	Select features according to the k highest scores.
SelectPercentile	Select features according to a percentile of the highest scores.
SelectorMixin	Transformer mixin that performs feature selection given a support mask
SequentialfeatureSelector	Transformer that performs Sequential feature Selection.
VarianceThreshold	feature selector that removes all low-variance features.
chi2	Compute chi-squared stats between each non-negative feature and class.
f_classif	Compute the ANOVA f-value for the provided sample.
f_regression	Univariate linear regression tests returning f-statistic and p-values.
mutual_info_classif	Estimate mutual information for a discrete target variable.
mutual_info_regression	Estimate mutual information for a continuous target variable.
r_regression	Compute Pearson's r for each features and the target.