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- feature_ranking(score)
- Rank features in descending order according to reliefF score, the higher the reliefF score, the more important the
feature is
- reliefF(X, y, **kwargs)
- This function implements the reliefF feature selection
Input
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X: {numpy array}, shape (n_samples, n_features)
input data
y: {numpy array}, shape (n_samples,)
input class labels
kwargs: {dictionary}
parameters of reliefF:
k: {int}
choices for the number of neighbors (default k = 5)
Output
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score: {numpy array}, shape (n_features,)
reliefF score for each feature
Reference
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Robnik-Sikonja, Marko et al. "Theoretical and empirical analysis of relieff and rrelieff." Machine Learning 2003.
Zhao, Zheng et al. "On Similarity Preserving Feature Selection." TKDE 2013.
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