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When can accuracy be misleading?

Answer and explanation

Correct answer: When the classes are highly imbalanced

Accuracy can be misleading when classes are highly imbalanced. A model that predicts only the majority class may obtain a high accuracy while performing poorly on the minority class. In such cases, precision, recall, F1 score, and the confusion matrix provide additional information about classification performance.

Related tags

Artificial IntelligenceClass 12Part B: Subject Specific SkillsUnit 2: Data Science MethodologyAccuracyImbalanced DataModel EvaluationPrecisionRecallF1 Score

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