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: Transform a new matrix using the selected features
code: def transform(self, X):
      """Transform a new matrix using the selected features"""
      mask = self.get_support()
      X = check_array(X)
      if len(mask) != X.shape[1]:
          raise ValueError(
              "X has a different shape than during fitting."
          )
      return check_array(X)[:, safe_mask(X, mask)]
  
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 5 for calling the BaseRandomizedLinearModel obj's underlying member method code with example usage: obj.transform(X) and returns: check_array