code: # Random Forest Regressor Example
  
  from sklearn.datasets import load_boston
  from sklearn.model_selection import train_test_split
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  
  
  def main():
  
      """
      Random Forest Regressor Example using sklearn function.
      Boston house price dataset is used to demonstrate the algorithm.
      """
  
      # Load Boston house price dataset
      boston = load_boston()
      print(boston.keys())
  
      # Split dataset into train and test data
      X = boston["data"]  # features
      Y = boston["target"]
      x_train, x_test, y_train, y_test = train_test_split(
          X, Y, test_size=0.3, random_state=1
      )
  
      # Random Forest Regressor
      rand_for = RandomForestRegressor(random_state=42, n_estimators=300)
      rand_for.fit(x_train, y_train)
  
      # Predict target for test data
      predictions = rand_for.predict(x_test)
      predictions = predictions.reshape(len(predictions), 1)
  
      # Error printing
      print(f"Mean Absolute Error:\t {mean_absolute_error(y_test, predictions)}")
      print(f"Mean Square Error  :\t {mean_squared_error(y_test, predictions)}")
  
  
  if __name__ == "__main__":
      main()
  
apis: sklearn.metrics.mean_squared_error sklearn.metrics.mean_absolute_error sklearn.datasets.load_boston sklearn.ensemble.RandomForestRegressor sklearn.model_selection.train_test_split