text: import math
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.metrics import mean_squared_error
  
  class Model:
  
      def __init__(self):
          # initialise model.
          self.model = RandomForestRegressor(random_state=67)
  
      def evaluate(self, X_train, y_train, X_test, y_test):
          """
          Fit Random Forest Classifier.        
          :param params: The parameters used to train the classifier.
          :param X_train: The training input examples.
          :param y_train: The target values. An array of int.
          :return: Train F1 score as a single float.
          """
          self.model.fit(X_train, y_train)
          y_pred = self.model.predict(X_test)
          score = mean_squared_error(y_test, y_pred)
          RMSE = math.sqrt(score)
          return print('The Test RMSE for Random Forest Regressor is {}' .format(RMSE))