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: Predictions with the model. Returns posterior means and standard deviations at X.
code: def predict(self, X):
      """
      Predictions with the model. Returns posterior means and standard deviations at X.
      """
      X = np.atleast_2d(X)
      m = np.empty(shape=(0, 1))
      s = np.empty(shape=(0, 1))
  
      for k in range(X.shape[0]):
          preds = []
          for pred in self.model.estimators_:
              preds.append(pred.predict(X[k, :])[0])
          m = np.vstack((m, np.array(preds).mean()))
          s = np.vstack((s, np.array(preds).std()))
      return m, s
  
file_path: GPyOpt/GPyOpt/models/rfmodel.py
imports: from .base import BOModel
  import numpy
label_desc: Use image node_id 4 for calling the RFModel obj's underlying member method code with example usage: obj.predict(X) and returns: m,  s