class_docstr: Base class for BoostSearch meta-estimator.
  
  Warning: This class should not be used directly. Use derived classes
  instead.
class_docstr_tok: ["Base","class","for","BoostSearch","meta-estimator",".","Warning",":","This","class","should","not","be","used","directly",".","Use","derived","classes","instead","."]
docstr: Predict X.
  
  Parameters
  ----------
  X : array-like of shape (n_samples, n_features)
      Samples.
  
  **predict_params : Additional predict arguments.
  
  Returns
  -------
  pred : ndarray of shape (n_samples,)
      The predicted values.
docstr_tok: ["Predict","X.","Parameters","--","--","--","--","--","X",":","array-like","of","shape","(","n_samples",",","n_features",")","Samples",".","*","*","predict_params",":","Additional","predict","arguments",".","Returns","--","--","--","-","pred",":","ndarray","of","shape","(","n_samples",",",")","The","predicted","values","."]
code: def predict(self, X, **predict_params):
      """Predict X.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Samples.
  
      **predict_params : Additional predict arguments.
  
      Returns
      -------
      pred : ndarray of shape (n_samples,)
          The predicted values.
      """
  
      check_is_fitted(self)
  
      if hasattr(self, "transform"):
          X = self.transform(X)
  
      return self.estimator_.predict(X, **predict_params)
  
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 5 for calling the _BoostSearch obj's underlying member method code with example usage: obj.predict(X) and returns: self