depynd.feature_selection._mifs()
¶
-
depynd.feature_selection.
_mifs
(X, y, lamb, k, **kwargs)¶ Select effective features in
X
on predictingy
using mutual-information-based feature selection [brown2012conditional].Parameters: - X (array-like, shape (n_samples, n_features)) – Observations of feature variables.
- y (array-like, shape (n_samples)) – Observations of the target variable.
- lamb (float or None) – Threshold for independence tests. Ignored if k is specified.
- k (int or None) – Number of selected features.
- kwargs (dict) – Optional parameters for MI estimation.
Returns: indices – Indices of the selected features.
Return type: References
[brown2012conditional] Brown, Gavin, et al. “Conditional likelihood maximisation: a unifying framework for information theoretic feature selection.” Journal of machine learning research 13.Jan (2012): 27-66.