When Accuracy Lies
Use base rate, confusion matrix, minority-class recall, and naive baseline to make a better ML project decision.
What is the strongest next move for When Accuracy Lies? Write base rate, confusion matrix, minority-class recall, and naive baseline first, then choose the simplest testable modeling step. Use the most powerful model available so performance can overcome messy setup. Keep the default metric and threshold because defaults are neutral. Delay all work until perfect data exists. The strong answer follows Google classification metrics by making the decision evidence explicit before optimizing. Right: it creates evidence and a baseline before complexity. Weaker: complexity amplifies unclear labels, metrics, or leakage. Weaker: defaults may violate the workflow constraint or mistake cost. Weaker:…
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