Tree-based AdaBoost regression
AdaBoost models are usually built with decision trees as the base estimators. Let's give this a try now and see if model performance improves even further.
We'll use twelve estimators as before to have a fair comparison. There's no need to instantiate the decision tree as it is the base estimator by default.
Cet exercice fait partie du cours
Ensemble Methods in Python
Instructions
- Build and fit an
AdaBoostRegressorusing12estimators. You do not have to specify a base estimator. - Calculate the predictions on the test set.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Build and fit a tree-based AdaBoost regressor
reg_ada = ____(____, random_state=500)
reg_ada.fit(X_train, y_train)
# Calculate the predictions on the test set
pred = ____
# Evaluate the performance using the RMSE
rmse = np.sqrt(mean_squared_error(y_test, pred))
print('RMSE: {:.3f}'.format(rmse))