sklearn.feature_selection.f_regression(X, y, center=True)[source]

Univariate linear regression tests.

Quick linear model for testing the effect of a single regressor, sequentially for many regressors.

This is done in 2 steps:

  1. The cross correlation between each regressor and the target is computed, that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) * std(y)).
  2. It is converted to an F score then to a p-value.

Read more in the User Guide.


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

center : True, bool,

If true, X and y will be centered.


F : array, shape=(n_features,)

F values of features.

pval : array, shape=(n_features,)

p-values of F-scores.

See also

ANOVA F-value between label/feature for classification tasks.
Chi-squared stats of non-negative features for classification tasks.