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} of shape (n_samples, n_features)

The set of regressors that will be tested sequentially.

yarray-like of shape (n_samples,)

The target vector.

Returns:

f_statisticndarray of shape (n_features,)

F-statistic for each feature.

p_valuesndarray of shape (n_features,)

P-values associated with the F-statistic.

See also

chi2

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

f_regression

F-value between label/feature for regression tasks.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.feature_selection import f_classif
>>> X, y = make_classification(
...     n_samples=100, n_features=10, n_informative=2, n_clusters_per_class=1,
...     shuffle=False, random_state=42
... )
>>> f_statistic, p_values = f_classif(X, y)
>>> f_statistic
array([2.2...e+02, 7.0...e-01, 1.6...e+00, 9.3...e-01,
       5.4...e+00, 3.2...e-01, 4.7...e-02, 5.7...e-01,
       7.5...e-01, 8.9...e-02])
>>> p_values
array([7.1...e-27, 4.0...e-01, 1.9...e-01, 3.3...e-01,
       2.2...e-02, 5.7...e-01, 8.2...e-01, 4.5...e-01,
       3.8...e-01, 7.6...e-01])

Gallery examples

Pipeline ANOVA SVM

Univariate Feature Selection

SVM-Anova: SVM with univariate feature selection