Which is a common evaluation metric for classification models?

Accuracy is a fundamental and widely-used evaluation metric for classification models. It measures the proportion of correct predictions made by the model out of the total number of predictions. Essentially, accuracy indicates how well the model performs overall, providing a straightforward metric when the classes are balanced and the cost of different types of errors is roughly equal.

In cases where the dataset contains a roughly equal distribution of classes, accuracy can be an effective indicator of performance; however, it may not be sufficient on its own in datasets with imbalanced classes. In such scenarios, metrics like precision, recall, and the F1 score also provide important insight into a model’s performance, albeit they are used in more specific contexts to address issues that accuracy might not fully capture.

For example, precision focuses on the accuracy of positive predictions, recall measures the ability of the model to capture all relevant positive cases, and the F1 score is the harmonic mean of precision and recall, providing a balance between the two. These metrics become critical in situations where the consequences of false positives and false negatives differ significantly, reinforcing that accuracy is just one part of the evaluation landscape for classification models.