sklearn.feature_selection.f_classif()

sklearn.feature_selection.f_classif(X, y) [source]

Compute the ANOVA F-value for the provided sample.

Read more in the User Guide.

Parameters:	X : {array-like, sparse matrix} shape = [n_samples, n_features] The set of regressors that will be tested sequentially. y : array of shape(n_samples) The data matrix.
Returns:	F : array, shape = [n_features,] The set of F values. pval : array, shape = [n_features,] The set of p-values.

See also

chi2

Chi-squared stats of non-negative features for classification tasks.

f_regression

F-value between label/feature for regression tasks.

Examples using sklearn.feature_selection.f_classif

Univariate Feature Selection

SVM-Anova: SVM with univariate feature selection