In the preceding sections, we trained several models. Now, we will compose them into an ensemble called a super learner using a deep learning model. The process to build a super learner is straightforward (see the preceding figure):
- Select base algorithms (for example, GLM, random forest, GBM, and so on).
- Select a meta-learning algorithm (for example, deep learning).
- Train each of the base algorithms on the training set.
- Perform K-fold cross-validation on each of these learners and collect the cross-validated predicted values from each of the base algorithms.
- The N cross-validated predicted values from each of the L-base algorithms can be combined to form a new NxL matrix. This matrix, along with the original response vector, is called the "level-one" data.
- Train the meta-learning algorithm on the level-one data.
- The super learner (or so-called "ensemble...
