Function f_classif

sklearn/feature_selection/_univariate_selection.py:125–171

Compute the ANOVA F-value for the provided sample. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The set of regressors that will be tested sequentially. y : arra

(X, y)

    prefer_skip_nested_validation=True,
)
def f_classif(X, y):
    """Compute the ANOVA F-value for the provided sample.

    Read more in the :ref:`User Guide <univariate_feature_selection>`.

    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        The set of regressors that will be tested sequentially.

    y : array-like of shape (n_samples,)
        The target vector.

    Returns
    -------
    f_statistic : ndarray of shape (n_features,)
        F-statistic for each feature.

    p_values : ndarray 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([2.21e+02, 7.02e-01, 1.70e+00, 9.31e-01,
           5.41e+00, 3.25e-01, 4.71e-02, 5.72e-01,
           7.54e-01, 8.90e-02])
    >>> p_values
    array([7.14e-27, 4.04e-01, 1.96e-01, 3.37e-01,
           2.21e-02, 5.70e-01, 8.29e-01, 4.51e-01,
           3.87e-01, 7.66e-01])
    """
    X, y = check_X_y(X, y, accept_sparse=["csr", "csc", "coo"])
    args = [X[safe_mask(X, y == k)] for k in np.unique(y)]
    return f_oneway(*args)


def _chisquare(f_obs, f_exp):