class_docstr: Base class for BoostRFA meta-estimator.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
class_docstr_tok: ["Base","class","for","BoostRFA","meta-estimator",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
code: def __init__(
      self,
      estimator,
      *,
      min_features_to_select=None,
      step=1,
      greater_is_better=False,
      importance_type="feature_importances",
      train_importance=True,
      verbose=0,
  ):
      self.estimator = estimator
      self.min_features_to_select = min_features_to_select
      self.step = step
      self.greater_is_better = greater_is_better
      self.importance_type = importance_type
      self.train_importance = train_importance
      self.verbose = verbose
  
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 1 to create a new _RFA object from inherited base classes: _BoostSelector with example: obj = _RFA(estimator)