, 2013 Lecture 12 : January 5

Abstract

In the previous lecture we saw the algorithm that builds a decision tree based on a sample. The decision tree is build until zero training error. As we saw before, our goal is to minimize the testing error and not the training error. In order to minimize the testing error, we have two basic options. The first option is to decide to do early stopping. Namely… (More)

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