class_docstr: Base class to implement randomized linear models for feature selection
  
  This implements the strategy by Meinshausen and Buhlman:
  stability selection with randomized sampling, and random re-weighting of
  the penalty.
docstr: Return a mask, or list, of the features/indices selected.
code: def get_support(self, indices=False):
      """Return a mask, or list, of the features/indices selected."""
      check_is_fitted(self, "scores_")
  
      mask = self.scores_ > self.selection_threshold
      return mask if not indices else np.where(mask)[0]
  
imports: import itertools
  from abc import ABCMeta, abstractmethod
  import warnings
  import numpy
  from scipy.sparse import issparse
  from scipy import sparse
  from scipy.interpolate import interp1d
  from .base import _preprocess_data
  from ..base import BaseEstimator, TransformerMixin
  from ..externals import six
  from ..externals.joblib import Memory, Parallel, delayed
  from ..utils import as_float_array, check_random_state, check_X_y, check_array, safe_mask
  from ..utils.validation import check_is_fitted
  from .least_angle import lars_path, LassoLarsIC
  from .logistic import LogisticRegression
  from ..exceptions import ConvergenceWarning
label_desc: Use image node_id 4 for calling the BaseRandomizedLinearModel obj's underlying member method code with example usage: obj.get_support(indices) and returns: unknown