25.1: Data mining vs. Machine learning

The terms “data mining” and “ML” have a lot of sloppy overlap, but one distinction we can pick out is this. If someone says they’re doing data mining, their goal is normally inference: deriving high-level strategic insights based on patterns in the data. Discovering that amateur pitching performances translate more reliably to the major leagues than amateur batting performances do, generally speaking, is an inference, and a potentially valuable find.

If someone says they’re doing ML, on the other hand, their goal is normally prediction: making an educated guess about how a specific case will turn out. When we forecast how many home runs we think a college prospect will hit in his first two years in the majors, we’re making a specific prediction rather than inferring a general truth – this, too, is potentially quite valuable, as it may lead us to decide to sign the player or look at different options.

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