class_docstr: Base class for BoostRFA meta-estimator.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
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docstr: Reduces the input X to the features selected with RFA.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      Samples.
  
  Returns
  -------
  X : array-like of shape (n_samples, n_features_)
      The input samples with only the selected features by Boruta.
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code: def transform(self, X):
      """Reduces the input X to the features selected with RFA.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Samples.
  
      Returns
      -------
      X : array-like of shape (n_samples, n_features_)
          The input samples with only the selected features by Boruta.
      """
  
      check_is_fitted(self)
  
      return self._transform(X, inverse=False)
  
imports: import io
  import contextlib
  import warnings
  import numpy
  import scipy
  from copy import deepcopy
  from sklearn.base import clone
  from sklearn.utils.validation import check_is_fitted
  from sklearn.base import BaseEstimator, TransformerMixin
  from joblib import Parallel, delayed
  from hyperopt import fmin, tpe
  from .utils import ParameterSampler, _check_param, _check_boosting
  from .utils import _set_categorical_indexes, _get_categorical_support
  from .utils import _feature_importances, _shap_importances
label_desc: Use image node_id 6 for calling the _RFA obj's underlying member method code with example usage: obj.transform(X) and returns: self