class_docstr: Base class for BoostRFE meta-estimator.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
class_docstr_tok: ["Base","class","for","BoostRFE","meta-estimator",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
docstr: Private method to validate and check fit_params.
docstr_tok: ["Private","method","to","validate","and","check","fit_params","."]
code: def _check_fit_params(self, fit_params):
      """Private method to validate and check fit_params."""
  
      _fit_params = deepcopy(fit_params)
      estimator = clone(self.estimator)
      # add here possible estimator checks in each iteration
  
      _fit_params = _set_categorical_indexes(
          self.support_, self._cat_support, _fit_params
      )
  
      if "eval_set" in _fit_params:
          _fit_params["eval_set"] = list(
              map(
                  lambda x: (self.transform(x[0]), x[1]),
                  _fit_params["eval_set"],
              )
          )
  
      if "feature_name" in _fit_params:  # LGB
          _fit_params["feature_name"] = "auto"
  
      if "feature_weights" in _fit_params:  # XGB  import warnings
          warnings.warn(
              "feature_weights is not supported when selecting features. "
              "It's automatically set to None."
          )
          _fit_params["feature_weights"] = None
  
      return _fit_params, estimator
  
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 2 for calling the _RFE obj's underlying member method code with example usage: obj._check_fit_params(fit_params) and returns: _fit_params,  estimator