Precision is a classification metric that measures the proportion of predicted positives that are actually positive. It is important when false positives are costly and is often considered alongside recall.
In classification , the precision for a class is the number of items correctly predicted as belonging to that class divided by the total number of items predicted as belonging to the class.
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.
A side-by-side comparison of Precision and Recall. Understand how precision evaluates the correctness of predicted positives, while recall evaluates how many actual positives were found.
A side-by-side comparison of Accuracy and Precision. Understand why overall correctness can hide weak positive-class performance and why precision matters when false positives are costly.