Which metric is frequently used to evaluate classification models?

Accuracy is a commonly used metric for evaluating classification models because it provides a straightforward measure of how many predictions made by the model are correct. This metric calculates the ratio of correctly predicted instances to the total number of instances, giving a clear indication of overall performance.

Accuracy is particularly useful in cases where the classes are balanced, meaning that there is a similar number of instances for each class. This makes it easy to interpret, as higher accuracy indicates better model performance. However, it may not be as informative in cases of class imbalance, where some metrics may provide more insight into how well the model is performing across different classes.

While precision, recall, and F1-score are also important metrics that evaluate specific aspects of model performance—such as the trade-off between false positives and false negatives—accuracy remains the foundational metric that is frequently considered in many classification tasks due to its simplicity and ease of understanding.