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When does a false positive occur?

Answer and explanation

Correct answer: The model predicts the positive class, but the actual class is negative.

In binary classification, a false positive is an error in which the model predicts the positive class even though the actual class is negative. For example, a system may flag a harmless email as spam. Option B is a true positive because both results are positive, option C is a true negative, and option D is a false negative. Therefore, A is correct.

Related tags

Artificial IntelligenceFalse PositiveConfusion MatrixClassificationModel EvaluationUnit 2: Data Science MethodologyUnit 2 Data Science MethodologyPart B: Subject Specific Skills

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