Use train to fit, validation to choose, test to estimate to make a better ML project decision.
Splits Have Different Jobs Splits protect teams from tuning against reported evidence. In this lesson, the concrete practice is to make train to fit, validation to choose, test to estimate visible before modeling decisions harden. Why it works: machine-learning systems fail when teams optimize a model before agreeing on the decision, evidence, constraint, or risk. Writing the practice down exposes disagreement early enough to change the label, feature set, split, metric, threshold, or launch plan. Mechanism: the model is only one part of the system. Labels define what it learns, features define what it can know, splits define how honestly…
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