sklearn.feature_selection.f_regression¶
- sklearn.feature_selection.f_regression(X, y, center=True)¶
Univariate linear regression tests
Quick linear model for testing the effect of a single regressor, sequentially for many regressors.
This is done in 3 steps: 1. the regressor of interest and the data are orthogonalized wrt constant regressors 2. the cross correlation between data and regressors is computed 3. it is converted to an F score then to a p-value
Parameters: X : {array-like, sparse matrix} shape = (n_samples, n_features)
The set of regressors that will tested sequentially.
y : array of shape(n_samples).
The data matrix
center : True, bool,
If true, X and y will be centered.
Returns: F : array, shape=(n_features,)
F values of features.
pval : array, shape=(n_features,)
p-values of F-scores.
