Stacked predictions for app ratings
Once the stacking estimator is built you can fit it to the training set. Then, it will be ready for step 5: use the stacked ensemble for predictions.
The stacking classifier is available to you as clf_stack.
Let's obtain the final predictions and see if there is any improvement in performance thanks to stacking.
This exercise is part of the course
Ensemble Methods in Python
Exercise instructions
- Fit the stacking classifier on the training set.
- Calculate the final predictions from the stacking estimator on the test set.
- Evaluate the performance on the test set using the accuracy score.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Fit the stacking classifier to the training set
____
# Obtain the final predictions from the stacking classifier
pred_stack = ____
# Evaluate the new performance on the test set
print('Accuracy: {:0.4f}'.format(____))