Precision ist eine Klassifikationsmetrik, die den Anteil vorhergesagter positiver Fälle misst, die tatsächlich positiv sind. Sie ist wichtig, wenn falsch positive Ergebnisse kostspielig sind, und wird häufig zusammen mit Recall betrachtet.
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.