Classification and regression are two key types of supervised learning. Classification predicts discrete categories, like whether an email is “spam or not spam,” or whether a runner is “injured or not injured” based on symptoms. Regression, on the other hand, predicts continuous values, such as estimating house prices or forecasting a runner’s finish time based on training data. The main difference is that classification assigns inputs into groups, while regression estimates a number.
In running, I see both in action: when I pace a runner, I’m doing a regression task, predicting how long he or she can finish at a steady effort. When I decide whether he or she should push or rest, that’s more like classification, choosing between two categories based on conditions.
Perceive that which cannot be seen with the eye.
Miyamoto Musashi
Both show how models, like coaches, guide decisions, but the type of prediction depends on the problem.
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