Decision trees are used in many different fields. In machine learning, they grew out of work in psychology. Earl Hunt and colleagues used them in the 1960s to model how humans acquire new concepts, and one of Hunt’s graduate students, J. Ross Quinlan, later tried using them for chess. His original goal was to predict the outcome of king-rook versus king-knight endgames from the board positions. From those humble beginnings, decision trees have grown to be, according to surveys, the most widely used machine-learning algorithm. It’s not hard to see why: they’re easy to understand, fast to learn,
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