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sklearn.feature_selection.f_regression(X, y, center=True)
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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:
- 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)).
- It is converted to an F score then to a p-value.
Read more in 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.
sklearn.feature_selection.f_regression()
Examples using
2017-01-15 04:26:06
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