skbold.feature_selection.selectors module¶
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fisher_criterion_score
(X, y, norm='l1', balance=False)[source]¶ Calculates fisher score.
See [1]_ for more info.
References
[1] P. E. H. R. O. Duda and D. G. Stork. Pattern Classification. Wiley-Interscience Publication, 2001.
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
- norm (str) – Whether to use the l1-norm or l2-norm.
Returns: scores_ – Fisher criterion scores for each feature.
Return type: array, shape=(n_features,)