In the context of machine learning, which statement describes precision?

Precision is defined as the fraction of relevant instances among all retrieved instances. This metric is particularly important in situations where the cost of false positives is high, meaning it accurately reflects the effectiveness of a model in identifying relevant instances.

When a model makes predictions, it may retrieve a certain number of positive instances, but not all of them will actually be relevant or true positives. Precision captures the ratio of true positives to the total number of instances the model classified as positive, helping to inform how reliable these positive predictions are.

This measure provides insight into the performance of the model concerning its positive predictions and is a vital component of evaluating models, especially in binary classification tasks, where the focus is on ensuring that when a positive result is predicted, it is indeed a positive outcome.

In contrast, other options discuss different aspects of model performance. The first option refers to recall, which focuses on the ability of the model to retrieve all relevant instances. The third option refers to overall accuracy, which combines both true positives and true negatives into a single metric but does not differentiate between the types of errors made. Lastly, the fourth option describes a related concept but lacks the specificity of what precision is defined as. Thus, it is crucial to understand that precision uniquely highlights the correctness