class_docstr: Select predefined feature subsets.
class_docstr_tok: ["Select","predefined","feature","subsets","."]
docstr: Get the boolean mask indicating which features are selected
  Returns
  -------
  support : boolean array of shape [# input features]
      An element is True iff its corresponding feature is selected for
      retention.
docstr_tok: ["Get","the","boolean","mask","indicating","which","features","are","selected","Returns","--","--","--","-","support",":","boolean","array","of","shape","[","#","input","features","]","An","element","is","True","iff","its","corresponding","feature","is","selected","for","retention","."]
code: def _get_support_mask(self):
      """
      Get the boolean mask indicating which features are selected
      Returns
      -------
      support : boolean array of shape [# input features]
          An element is True iff its corresponding feature is selected for
          retention.
      """
  
      n_features = len(self.feature_names)
      mask = np.zeros(n_features, dtype=bool)
      mask[np.asarray(self.feat_list_idx)] = True
  
      return mask
  
file_path: tpot/tpot/builtins/feature_set_selector.py
imports: import numpy
  import pandas
  import os, os.path
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 5 for calling the FeatureSetSelector obj's underlying member method code with example usage: obj._get_support_mask() and returns: mask