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.
