PYTHON

gradient boosting regressor

# Import GradientBoostingRegressor
from sklearn.ensemble import GradientBoostingRegressor

# Instantiate gb
gb = GradientBoostingRegressor(max_depth=4,
                               n_estimators=200,
                               random_state=2)
# Fit gb to the training set
gb.fit(X_train, y_train)

# Predict test set labels
y_pred = gb.predict(X_test)
# Import mean_squared_error as MSE
from sklearn.metrics import mean_squared_error as MSE

# Compute MSE
mse_test = MSE(y_test, y_pred)

# Compute RMSE
rmse_test = mse_test**(1/2)

# Print RMSE
print('Test set RMSE of gb: {:.3f}'.format(rmse_test))