sklearn.feature_selection.f_regression

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

Univariate linear regression tests.
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.
This is done in 2 steps:
 The correlation between each regressor and the target is computed, that is, ((X[:, i]  mean(X[:, i])) * (y  mean_y)) / (std(X[:, i]) * std(y)).
 It is converted to an F score then to a pvalue.
For more on usage see the User Guide.
Parameters: 

X : {arraylike, 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. 
Returns: 

F : array, shape=(n_features,) 
F values of features. 
pval : array, shape=(n_features,) 
pvalues of Fscores. 
See also

mutual_info_regression
 Mutual information for a continuous target.

f_classif
 ANOVA Fvalue between label/feature for classification tasks.

chi2
 Chisquared stats of nonnegative features for classification tasks.

SelectKBest
 Select features based on the k highest scores.

SelectFpr
 Select features based on a false positive rate test.

SelectFdr
 Select features based on an estimated false discovery rate.

SelectFwe
 Select features based on familywise error rate.

SelectPercentile
 Select features based on percentile of the highest scores.
Examples using sklearn.feature_selection.f_regression