True positives and False positives are correct.

True positives count the instances that are actually positive and that the classifier also predicted as positive, so this measure directly shows how the model behaves on positive cases and relates to sensitivity and recall.

False positives count the instances that are actually negative but that the classifier predicted as positive, so this measure directly shows how often the model mistakenly labels negative cases as positive and relates to specificity and precision trade offs.

Area under the ROC curve is not the best answer for this question because it is an aggregate, threshold independent measure of ranking performance rather than a direct count of how the model classifies positive and negative cases.

Mean absolute error is incorrect because it is a regression error metric that measures average absolute difference between predicted and actual numeric values and it does not describe classification behavior on positive and negative cases.

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