PYTHON

sample stochastic gradient boosting regressor algorithm

# Import GradientBoostingRegressor
from sklearn.ensemble import GradientBoostingRegressor

# Instantiate sgbr
sgbr = GradientBoostingRegressor(max_depth=4, 
                                 subsample=0.9,
                                 max_features=0.75,
                                 n_estimators=200,
                                 random_state=2)
# Fit sgbr to the training set
sgbr.fit(X_train, y_train)

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

# Compute test set MSE
mse_test = MSE(y_test, y_pred)

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

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