skbold.feature_selection.selectors module

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,)