<ul><li>The accuracy trap: Achieving high accuracy may not guarantee effective model evaluation if there is class imbalance.</li><li>The precision-recall nightmare: A high precision but low recall model can lead to missing actual cases.</li><li>The F1-score fallacy: Opting for a balanced F1-score may mask flaws in evaluation strategy, causing significant consequences.</li><li>The dark side of model evaluation: Wrong metric choices in healthcare, finance, and e-commerce can result in costly mistakes and missed opportunities.</li></ul>

The Dark Side of Model Evaluation That Nobody Talks About

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