Labels Are Claims About Reality
Use event definition, time window, owner, bias note, and exclusions to make a better ML project decision.
Labels Are Claims About Reality A supervised model learns the label you define. In this lesson, the concrete practice is to make event definition, time window, owner, bias note, and exclusions 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…
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