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sklearn.feature_selection.f_classif(X, y)
[source] -
Compute the ANOVA F-value for the provided sample.
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.
Returns: F : array, shape = [n_features,]
The set of F values.
pval : array, shape = [n_features,]
The set of p-values.
See also
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chi2
- Chi-squared stats of non-negative features for classification tasks.
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f_regression
- F-value between label/feature for regression tasks.
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sklearn.feature_selection.f_classif()
Examples using
2017-01-15 04:26:06
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