What metric evaluates the fraction of true positive instances among all the positive predictions?

The correct answer is precision. Precision measures the ratio of true positive instances to the total number of instances that were predicted as positive. In other words, it evaluates how many of the positively identified instances are actually correct.

Precision is particularly important in scenarios where the cost of false positives is high, as it helps gauge the reliability of the positive predictions made by the model. It focuses on the quality of the positive predictions, ensuring that when a prediction indicates a positive outcome, there is a high likelihood that it is indeed a true positive.

In contrast, accuracy measures the overall correctness of the model across both positive and negative classes, which can be misleading, especially in cases with imbalanced datasets. Recall, on the other hand, focuses on the ability of the model to identify all relevant instances (true positives) among the actual positives, rather than evaluating the predicted positives. Specificity is concerned with the model's ability to correctly identify all actual negatives and does not relate to positive predictions.