class_docstr: Randomized Logistic Regression
  
  Randomized Logistic Regression works by subsampling the training
  data and fitting a L1-penalized LogisticRegression model where the
  penalty of a random subset of coefficients has been scaled. By
  performing this double randomization several times, the method
  assigns high scores to features that are repeatedly selected across
  randomizations. This is known as stability selection. In short,
  features selected more often are considered good features.
  
  Read more in the :ref:`User Guide <randomized_l1>`.
  
  Parameters
  ----------
  C : float, optional, default=1
      The regularization parameter C in the LogisticRegression.
  
  scaling : float, optional, default=0.5
      The s parameter used to randomly scale the penalty of different
      features (See :ref:`User Guide <randomized_l1>` for details ).
      Should be between 0 and 1.
  
  sample_fraction : float, optional, default=0.75
      The fraction of samples to be used in each randomized design.
      Should be between 0 and 1. If 1, all samples are used.
  
  n_resampling : int, optional, default=200
      Number of randomized models.
  
  selection_threshold : float, optional, default=0.25
      The score above which features should be selected.
  
  fit_intercept : boolean, optional, default=True
      whether to calculate the intercept for this model. If set
      to false, no intercept will be used in calculations
      (e.g. data is expected to be already centered).
  
  verbose : boolean or integer, optional
      Sets the verbosity amount
  
  normalize : boolean, optional, default False
      If True, the regressors X will be normalized before regression.
      This parameter is ignored when `fit_intercept` is set to False.
      When the regressors are normalized, note that this makes the
      hyperparameters learnt more robust and almost independent of the number
      of samples. The same property is not valid for standardized data.
      However, if you wish to standardize, please use
      `preprocessing.StandardScaler` before calling `fit` on an estimator
      with `normalize=False`.
  
  tol : float, optional, default=1e-3
       tolerance for stopping criteria of LogisticRegression
  
  n_jobs : integer, optional
      Number of CPUs to use during the resampling. If '-1', use
      all the CPUs
  
  random_state : int, RandomState instance or None, optional (default=None)
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance used
      by `np.random`.
  
  pre_dispatch : int, or string, optional
      Controls the number of jobs that get dispatched during parallel
      execution. Reducing this number can be useful to avoid an
      explosion of memory consumption when more jobs get dispatched
      than CPUs can process. This parameter can be:
  
          - None, in which case all the jobs are immediately
            created and spawned. Use this for lightweight and
            fast-running jobs, to avoid delays due to on-demand
            spawning of the jobs
  
          - An int, giving the exact number of total jobs that are
            spawned
  
          - A string, giving an expression as a function of n_jobs,
            as in '2*n_jobs'
  
  memory : Instance of joblib.Memory or string
      Used for internal caching. By default, no caching is done.
      If a string is given, it is the path to the caching directory.
  
  Attributes
  ----------
  scores_ : array, shape = [n_features]
      Feature scores between 0 and 1.
  
  all_scores_ : array, shape = [n_features, n_reg_parameter]
      Feature scores between 0 and 1 for all values of the regularization         parameter. The reference article suggests ``scores_`` is the max         of ``all_scores_``.
  
  Examples
  --------
  >>> from sklearn.linear_model import RandomizedLogisticRegression
  >>> randomized_logistic = RandomizedLogisticRegression()
  
  Notes
  -----
  See examples/linear_model/plot_sparse_recovery.py for an example.
  
  References
  ----------
  Stability selection
  Nicolai Meinshausen, Peter Buhlmann
  Journal of the Royal Statistical Society: Series B
  Volume 72, Issue 4, pages 417-473, September 2010
  DOI: 10.1111/j.1467-9868.2010.00740.x
  
  See also
  --------
  RandomizedLasso, LogisticRegression
code: def _make_estimator_and_params(self, X, y):
      params = dict(
          C=self.C, tol=self.tol, fit_intercept=self.fit_intercept
      )
      return _randomized_logistic, params
  
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 2 for calling the RandomizedLogisticRegression obj's underlying member method code with example usage: obj._make_estimator_and_params(X, y) and returns: _randomized_logistic,  params