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scikit-learn 1.3.2
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# `sklearn.feature_selection`.r_regression

sklearn.feature_selection.r_regression(X, y, *, center=True, force_finite=True)

  Compute Pearson’s r for each features and the target.

  Pearson’s r is also known as the Pearson correlation coefficient.

  Linear model for testing the individual effect of each of many regressors. This is a scoring function to be used in a feature selection procedure, not a free standing feature selection procedure.

  The cross correlation between each regressor and the target is computed as:

      E[(X[:, i] - mean(X[:, i])) * (y - mean(y))] / (std(X[:, i]) * std(y))

  For more on usage see the User Guide.

  New in version 1.0.

  Parameters:
    X{array-like, sparse matrix} of shape (n_samples, n_features)

      The data matrix.

    y array-like of shape (n_samples,)

      The target vector.

    center bool, default=True

      Whether or not to center the data matrix `X` and the target vector `y`. By default, `X` and `y` will be centered.

    force_finite bool, default=True

      Whether or not to force the Pearson’s R correlation to be finite. In the particular case where some features in `X` or the target `y` are constant, the Pearson’s R correlation is not defined. When `force_finite=False`, a correlation of `np.nan` is returned to acknowledge this case. When `force_finite=True`, this value will be forced to a minimal correlation of `0.0`.

      New in version 1.1.

  Returns:
    correlation_coefficient ndarray of shape (n_features,)

      Pearson’s R correlation coefficients of features.

  See also

  `f_regression`

    Univariate linear regression tests returning f-statistic and p-values.

  `mutual_info_regression`

    Mutual information for a continuous target.

  `f_classif`

    ANOVA F-value between label/feature for classification tasks.

  `chi2`

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