

def f_classif(X, y):
    'Compute the ANOVA F-value for the provided sample.\n\n    Read more in the :ref:`User Guide <univariate_feature_selection>`.\n\n    Parameters\n    ----------\n    X : {array-like, sparse matrix} shape = [n_samples, n_features]\n        The set of regressors that will be tested sequentially.\n\n    y : array of shape(n_samples)\n        The data matrix.\n\n    Returns\n    -------\n    F : array, shape = [n_features,]\n        The set of F values.\n\n    pval : array, shape = [n_features,]\n        The set of p-values.\n\n    See also\n    --------\n    chi2: Chi-squared stats of non-negative features for classification tasks.\n    f_regression: F-value between label/feature for regression tasks.\n    '
    (X, y) = check_X_y(X, y, ['csr', 'csc', 'coo'])
    args = [X[safe_mask(k, (y == k))] for k in np.unique(y)]
    return f_oneway(*args)

dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/feature_selection/univariate_selection.py f_classif/VarMisuse@42/335 `X`->`k`