Start with the Business Question
Use decision owner, action, prediction horizon, and cost of being wrong to make a better ML project decision.
Start with the Business Question CRISP-DM business understanding keeps models tied to decisions. In this lesson, the concrete practice is to make decision owner, action, prediction horizon, and cost of being wrong 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…
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