The metalearner can itself be any learner, from a decision tree to a simple weighted vote. To learn the weights, or the decision tree, we replace the attributes of each original example by the learners’ predictions. Learners that often predict the correct class will get high weights, and inaccurate ones will tend to be ignored. With a decision tree, the choice of whether to use a learner can be contingent on other learners’ predictions. Either way, to obtain a learner’s prediction for a given training example, we must first apply it to the original training set excluding that example and use
...more
