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8.8.10. 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 of shape (n_samples, n_features)

the set of regressors sthat will tested sequentially

y : array of shape(n_samples)

the data matrix

center : True, bool,

If true, X and y are centered

Returns :

F : array of shape (m),

the set of F values

pval : array of shape(m)

the set of p-values