depynd.feature_selection._mrmr()
¶
-
depynd.feature_selection.
_mrmr
(X, y, lamb, k, **kwargs)¶ Select effective features in
X
on predictingy
using minimum redundancy maximum relevance feature selection [peng2005feature].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
[peng2005feature] Peng, Hanchuan, Fuhui Long, and Chris Ding. “Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy.” IEEE Transactions on pattern analysis and machine intelligence 27.8 (2005): 1226-1238.