Artificial Intelligence Programming Practice Exam 2025 - Free AI Programming Practice Questions and Study Guide

Precision in the context of model evaluation is defined as the proportion of true positive results among all positive predictions made by the model. It specifically measures the accuracy of the positive predictions, indicating how many of the predicted positive cases are actually positive. This metric is particularly important in scenarios where the costs of false positives are high, as it helps assess the reliability of the model's positive classifications.

In a practical example, consider a medical testing scenario where a test for a disease produces positive results. High precision means that when the test indicates a patient has the disease, there's a high likelihood that they truly do. This is crucial for avoiding unnecessary anxiety and treatment associated with false positives.

The other options do not define precision correctly. The proportion of true negative results relates to specificity, which measures the true negatives among all actual negatives. Overall accuracy considers both true positives and negatives, but it does not exclusively measure the correctness of positive predictions. Finally, the availability of data for training is not a metric tied to model evaluation but rather a factor that influences model performance. Thus, the definition of precision aligns solely with the proportion of true positives among predicted positives.