What does precision measure in predictive analytics?

Precision measures the ratio of true positives to the total positives predicted. In other words, it indicates how many of the predicted positive cases were actually correct. This metric is particularly important in situations where the cost of false positives is high, as it helps to evaluate the reliability of the positive predictions made by a model. High precision means that when the model predicts a positive outcome, it is very likely to be a true positive, thus reflecting a well-performing model in terms of minimizing false positives.

For instance, in a medical diagnosis scenario, a model with high precision would suggest that when it indicates a patient is likely to have a certain condition, it is frequently correct. Hence, the focus on precision is crucial for informing decisions where the consequences of incorrect predictions could be significant.