Use feature, label, model, metric, objective, and baseline to make a better ML project decision.
What is the core move in Quick Reference: Core ML Vocabulary? Make feature, label, model, metric, objective, and baseline explicit before modeling. Precise vocabulary helps teams debug the right system part. What is the common trap? Optimizing the model before validating the decision evidence. The model can look better while the system becomes less useful. Framework-first vs algorithm-first Use the framework-first path for launch work. "Can we just use the best-performing model?" Your line Only after we verify feature, label, model, metric, objective, and baseline; otherwise the best score may optimize the wrong thing. Treating offline performance as the whole…
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