'loss': 'ls'
}
clf = ensemble.GradientBoostingRegressor(**params)
clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
print('\nGradientBoostingRegressor Model MSE (Full Dataset)')
print("Mean Squared Error: %.4f" % mse)
############################################################################################
### Create Relative Importance and Training-Test Set Deviance
############################################################################################
# Plot training deviance
# compute test set deviance
test_score = np.zeros((params['n_estimators'], ), dtype=np.float64)
for i, y_pred in enumerate(clf.staged_predict(X_test)):
test_score[i] = clf.loss_(y_test, y_pred)
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.title('Deviance')
plt.plot(np.arange(params['n_estimators']) + 1,
clf.train_score_,
'b-',
label='Training Set Deviance')
plt.plot(np.arange(params['n_estimators']) + 1,
test_score,
'r-',
label='Test Set Deviance')
plt.legend(loc='upper right')
plt.xlabel('Boosting Iterations')
plt.ylabel('Deviance')
