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# create an object of the RandomForestRegressor
model_RFR = RandomForestRegressor(max_depth=10)
# fit the model with the training data
model_RFR.fit(train_x, train_y)
# predict the target on train and test data
predict_train = model_RFR.predict(train_x)
predict_test = model_RFR.predict(test_x)
# Root Mean Squared Error on train and test data
print('RMSE on train data: ', mean_squared_error(train_y, predict_train)**(0.5))
print('RMSE on test data: ', mean_squared_error(test_y, predict_test)**(0.5))
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