Sometimes a user may wish to require certain features to be present in the feature space (for inference purposes/interpretability, etc.) and determine what other features minimize redundancy and maximize relevance with those features present.
Suggestion: revise mrmr_regression/classif function to take an optional list of k "fixed_features" such that the function returns a list of N features that always includes those k features and N-k other features that minimize redundancy/maximize relevance in the presence of those k fixed features.
Sometimes a user may wish to require certain features to be present in the feature space (for inference purposes/interpretability, etc.) and determine what other features minimize redundancy and maximize relevance with those features present.
Suggestion: revise mrmr_regression/classif function to take an optional list of k "fixed_features" such that the function returns a list of N features that always includes those k features and N-k other features that minimize redundancy/maximize relevance in the presence of those k fixed features.