sklearn.feature_selection.f_regression¶
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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 p-value.
For more on usage see the User Guide.
Parameters: - 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.
Returns: - F : array, shape=(n_features,)
F values of features.
- pval : array, shape=(n_features,)
p-values of F-scores.
See also
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.
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 family-wise error rate.
SelectPercentile- Select features based on percentile of the highest scores.
