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Why High Accuracy Can Be Misleading on Imbalanced Data

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Why High Accuracy Can Be Misleading on Imbalanced Data

EDITOR BRIEF

A model can show 99.4% accuracy on a fraud dataset simply by always predicting “not fraud,” yet it catches no fraud at all. The excerpt explains that accuracy can hide failure when one class is rare, and that precision and recall reveal more useful behavior. A logistic regression model has lower accuracy but much higher recall.

INSIGHTS

If you are learning ML, don’t rely on accuracy alone for skewed datasets. Try comparing precision, recall, and F1 on a small fraud or spam example to see how each metric changes.

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