IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

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What characterizes a false positive in spam detection?

A message marked correctly as spam.

A legitimate message incorrectly marked as spam.

In spam detection, a false positive occurs when a legitimate message is mistakenly identified as spam. This is particularly important because it can lead to important communications being missed by the recipient. Effective spam filters aim to minimize false positives as they can significantly impact user experience and lead to the loss of important emails, such as notifications from colleagues or personal messages.

The other choices do not align with the definition of a false positive. For instance, correctly marking a spam message or correctly classifying a message does not involve misclassification, which is the essence of a false positive. Additionally, a high recall rate refers to how well a model identifies all relevant instances (spam) but does not relate directly to classification errors involving legitimate messages.

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A message classified correctly.

A high recall rate.

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