esem.utils.get_param_mask

esem.utils.get_param_mask(X, y, criterion='bic', **kwargs)

Determine the most relevant parameters in the input space using a regularised linear model and either the Aikake or Baysian Information Criterion.

Parameters

  • X (array-like of shape (n_samples, n_features)) – Parameter values

  • y (array-like of shape (n_samples,)) – target values.

  • criterion ({'bic', 'aic'}, default 'bic') – The information criteria to apply for parameter selection. Either Aikake or Baysian Information Criterion.

  • kwargs (dict) – Further arguments for sklearn.feature_selection.SelectFromModel

Returns

mask (ndarray) – A boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention.