sklearn.feature_selection.f_classif¶
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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.
