Which metric is commonly used to evaluate classification models?

The commonly used metric for evaluating classification models is accuracy. Accuracy represents the proportion of true results—both true positives and true negatives—out of the total number of cases examined. This means that accuracy provides a straightforward measure of how well the classification model performs overall in making correct predictions.

In classification tasks, the goal is to assign labels or categories to input data points, and accuracy quantifies the model's effectiveness in correctly categorizing these points. A higher accuracy indicates that the model is better at predicting the correct class labels, which is particularly valuable when the classes are balanced.

While other metrics exist that assess classification models (such as precision, recall, and F1 score), they are often evaluated in conjunction with accuracy to provide a more comprehensive view of a model's performance, especially in cases of imbalanced classes. Therefore, the focus on accuracy as a primary metric makes it a fundamental concept in the evaluation of classification models.