class_docstr: Tranformer mixin that performs feature selection given a support mask
  
  This mixin provides a feature selector implementation with `transform` and
  `inverse_transform` functionality given an implementation of
  `_get_support_mask`.
class_docstr_tok: ["Tranformer","mixin","that","performs","feature","selection","given","a","support","mask","This","mixin","provides","a","feature","selector","implementation","with","`","transform","`","and","`","inverse_transform","`","functionality","given","an","implementation","of","`","_get_support_mask","`","."]
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.
      """
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/feature_selection/base.py
imports: from abc import ABCMeta, abstractmethod
  from warnings import warn
  import numpy
  from scipy.sparse import issparse, csc_matrix
  from ..base import TransformerMixin
  from ..utils import check_array, safe_mask
  from ..externals import six
label_desc: Use image node_id 2 for calling the SelectorMixin obj's underlying member method code with example usage: obj._get_support_mask() without return types