Precision

Precision is a key performance metric used in machine learning and statistics to evaluate the accuracy of a classification model. It measures the proportion of true positive predictions among all positive predictions made by the model. High precision indicates that when the model predicts a positive class, it is likely to be correct, which is particularly important in contexts where false positives carry significant consequences, such as in medical diagnoses or fraud detection. Precision is often used in conjunction with recall to provide a more comprehensive view of a model's performance, especially in imbalanced datasets where one class may dominate the other.