ielearn.predict.feature_selection module

class ielearn.predict.feature_selection.SelectThreshold(score_func=<function f_classif>, thresh=0.1)

Bases: sklearn.feature_selection.univariate_selection._BaseFilter

Select features according to the threshold.

Read more in the User Guide.

score_func : callable

Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues) or a single array with scores. Default is f_classif (see below “See also”). The default function only works with classification tasks.

thresh : int or “none”, optional, default=0.1

Score threshold for feature inclusion. The “none” option bypasses selection, for use in a parameter search.

scores_ : array-like, shape=(n_features,)

Scores of features.

pvalues_ : array-like, shape=(n_features,)

p-values of feature scores, None if score_func returned only scores.

Ties between features with equal scores will be broken in an unspecified way.

f_classif: ANOVA F-value between label/feature for classification tasks. mutual_info_classif: Mutual information for a discrete target. chi2: Chi-squared stats of non-negative features for classification tasks. f_regression: F-value between label/feature for regression tasks. mutual_info_regression: Mutual information for a continuous target. SelectPercentile: Select features based on percentile of the highest scores. SelectFpr: Select features based on a false positive rate test. SelectFdr: Select features based on an estimated false discovery rate. SelectFwe: Select features based on family-wise error rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.