sklearn.feature_selection.f_classif

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

Compute the ANOVA F-value for the provided sample.

Parameters:

X : {array-like, sparse matrix} shape = [n_samples, n_features]

The set of regressors that will 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