class_docstr: General class for handling a Random Forest in GPyOpt.
  
  .. Note:: The model has beed wrapper 'as it is' from  Scikit-learn. Check
  http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html
  for further details.
docstr: Creates the model given some input data X and Y.
code: def _create_model(self, X, Y):
      """
      Creates the model given some input data X and Y.
      """
      from sklearn.ensemble import RandomForestRegressor
  
      self.X = X
      self.Y = Y
      self.model = RandomForestRegressor(
          bootstrap=self.bootstrap,
          criterion=self.criterion,
          max_depth=self.max_depth,
          max_features=self.max_features,
          max_leaf_nodes=self.max_leaf_nodes,
          min_samples_leaf=self.min_samples_leaf,
          min_samples_split=self.min_samples_split,
          min_weight_fraction_leaf=self.min_weight_fraction_leaf,
          n_estimators=self.n_estimators,
          n_jobs=self.n_jobs,
          oob_score=self.oob_score,
          random_state=self.random_state,
          verbose=self.verbose,
          warm_start=self.warm_start,
      )
  
      # self.model = RandomForestRegressor()
      self.model.fit(X, Y.flatten())
  
file_path: GPyOpt/GPyOpt/models/rfmodel.py
imports: from .base import BOModel
  import numpy
label_desc: Use image node_id 2 for calling the RFModel obj's underlying member method code with example usage: obj._create_model(X, Y) without return types