Making the most of AdaBoost
As you have seen, for predicting movie revenue, AdaBoost gives the best results with decision trees as the base estimator.
In this exercise, you'll specify some parameters to extract even more performance. In particular, you'll use a lower learning rate to have a smoother update of the hyperparameters. Therefore, the number of estimators should increase. Additionally, the following features have been added to the data: 'runtime', 'vote_average', and 'vote_count'.
Questo esercizio fa parte del corso
Ensemble Methods in Python
Istruzioni dell'esercizio
- Build an
AdaBoostRegressorusing100estimators and a learning rate of0.01. - Fit
reg_adato the training set and calculate the predictions on the test set.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Build and fit an 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))