precision
A metric for classification models that answers the following question: When the model predicted the positive class, what percentage of the predictions were correct?
Plain English Explanation
A metric for classification models that answers the following question: When the model predicted the positive class, what percentage of the predictions were correct? Here is the formula:
where: - true positive means the model correctly predicted the positive class. - false positive means the model mistakenly predicted the positive class. For example, suppose a model made 200 positive predictions. Of these 200 positive predictions: - 150 were true positives. - 50 were false positives. In this case:
Contrast with accuracy and recall. See Classification: Accuracy, recall, precision and related metrics in Machine Learning Crash Course for more information.
How is it used?
Practitioners refer to precision when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.