Battery: Train Test Part 1
We will discuss the stability of CART trees in a binary classification problem. The algorithm builds a tree on the learn sample of data, and at times the results you have will not hold for the test sample. Therefore, you have the problem of finding the optimal balance and stability that will generalize for both the learn and test sample performance. In this example, we will use an email spam dataset and attempt to classify whether an email is spam or not spam. With the use of TRAIN TEST, CART will automatically scan every tree available and summarize the consistency between the learn and test performance in an easy-to-read table. Each tree will indicate if it agrees or disagrees based on direction and rank.
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