Mastering pandas - Second Edition

Decision trees

The basic idea behind decision trees is to use the training dataset to create a tree of decisions to make a prediction.

It recursively splits the training dataset into subsets based on the value of a single feature. Each split corresponds to a node in the decision tree. The splitting process is continued until every subset is pure; that is, all elements belong to a single class. This always works except in cases where there are duplicate training examples that fall into different classes. In this case, the majority class wins.

The end result is a ruleset for making predictions on the test dataset.

Decision trees encode a sequence of binary choices in a process that mimics how a human might classify things, but decide which ...