Movie revenue prediction with CatBoost
Let's finish up this chapter on boosting by returning to the movies dataset! In this exercise, you'll build a CatBoostRegressor to predict the log-revenue. Remember that our best model so far is the AdaBoost model with a RMSE of 5.15.
Will CatBoost beat AdaBoost? We'll try to use a similar set of parameters to have a fair comparison.
Recall that these are the features we have used so far: 'budget', 'popularity', 'runtime', 'vote_average', and 'vote_count'. catboost has been imported for you as cb.
OBS: be careful not to use a classifier, or your session might expire!
Cet exercice fait partie du cours
Ensemble Methods in Python
Instructions
- Build and fit a
CatBoostRegressorusing100estimators, a learning rate of0.1, and a max depth of3. - Calculate the predictions for the test set and print the RMSE.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
import catboost as cb
# Build and fit a CatBoost regressor
reg_cat = ____.____(____, ____, ____, random_state=500)
____
# Calculate the predictions on the test set
pred = ____
# Evaluate the performance using the RMSE
rmse_cat = np.sqrt(mean_squared_error(y_test, pred))
print('RMSE (CatBoost): {:.3f}'.format(rmse_cat))