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What does a false positive represent in a confusion matrix?

Answer and explanation

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

A false positive occurs when the actual class is negative, but the model labels it as positive. Therefore, option A is correct. For example, incorrectly identifying a person without a disease as disease-positive is a false positive and may lead to unnecessary follow-up testing. Option B is a true positive, whereas option D represents a false negative.

Related tags

False PositiveConfusion MatrixClassificationModel EvaluationData Science

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