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.
code: def __init__(
      self,
      bootstrap=True,
      criterion="mse",
      max_depth=None,
      max_features="auto",
      max_leaf_nodes=None,
      min_samples_leaf=1,
      min_samples_split=2,
      min_weight_fraction_leaf=0.0,
      n_estimators=500,
      n_jobs=1,
      oob_score=False,
      random_state=None,
      verbose=0,
      warm_start=False,
  ):
      self.bootstrap = bootstrap
      self.criterion = criterion
      self.max_depth = max_depth
      self.max_features = max_features
      self.max_leaf_nodes = max_leaf_nodes
      self.min_samples_leaf = min_samples_leaf
      self.min_samples_split = min_samples_split
      self.min_weight_fraction_leaf = min_weight_fraction_leaf
      self.n_estimators = n_estimators
      self.n_jobs = n_jobs
      self.oob_score = oob_score
      self.random_state = random_state
      self.verbose = verbose
      self.warm_start = warm_start
  
      self.model = None
  
file_path: GPyOpt/GPyOpt/models/rfmodel.py
imports: from .base import BOModel
  import numpy
label_desc: Use image node_id 1 to create a new RFModel object from inherited base classes: BOModel with example: obj = RFModel(bootstrap, criterion, max_depth, max_features, max_leaf_nodes, min_samples_leaf, min_samples_split, min_weight_fraction_leaf, n_estimators, n_jobs, oob_score, random_state, verbose, warm_start)