LoslegenKostenlos loslegen

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.

Diese Übung ist Teil des Kurses

Ensemble Methods in Python

Kurs anzeigen

Anleitung zur Übung

  • Build and fit an AdaBoostRegressor using 12 estimators. You do not have to specify a base estimator.
  • Calculate the predictions on the test set.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# 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))
Code bearbeiten und ausführen