Use source event, creation time, serving path, and leakage risk to make a better ML project decision.
Features Must Exist Before the Decision A feature is valid only if it exists at prediction time. In this lesson, the concrete practice is to make source event, creation time, serving path, and leakage risk 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…
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