Quick Reference: Classification Metrics
Use accuracy, precision, recall, specificity, and F1 to make a better ML project decision.
What is the core move in Quick Reference: Classification Metrics? Make accuracy, precision, recall, specificity, and F1 explicit before modeling. Each metric answers a different denominator question. 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 accuracy, precision, recall, specificity, and F1; otherwise the best score may optimize the wrong thing. Treating offline performance as the whole product. It redirects to decision…
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