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- f_score(X, y)
- This function implements the anova f_value feature selection (existing method for classification in scikit-learn),
where f_score = sum((ni/(c-1))*(mean_i - mean)^2)/((1/(n - c))*sum((ni-1)*std_i^2))
Input
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X: {numpy array}, shape (n_samples, n_features)
input data
y : {numpy array},shape (n_samples,)
input class labels
Output
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F: {numpy array}, shape (n_features,)
f-score for each feature
- feature_ranking(F)
- Rank features in descending order according to f-score, the higher the f-score, the more important the feature is
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