Exercise

# Stacking the base learners

Given that many Machine Learning problems are quite challenging to solve for a single learning method, you ought to know how to build stronger solvers through the magic of *ensemble models*. Stacking is a simple yet quite powerful technique for building ensemble models. A stacked ensemble model is a meta-learner since it learns to predict the outcome not from the original data but from the predictions returned by the set of base learners.

In this exercise you will build a stacked ensemble out of the four base learners in `models`

. The `caret`

package has been preloaded and the `control`

object holds the `trainControl`

specs used to learn the base models.

Instructions 1/4

**undefined XP**

#### Question

What are the components of an stacked ensemble model?

##### Possible Answers

- Set of base models and their predictions.
- Correlation matrix for the predictions made by the base models and a regression algorithm.
- Error rates from base models and error rate of a supervisor model.
- Predictions from base models and a supervisor model that learns how to best combine their predictions.