Which of the following is a common evaluation metric for classification models?

Accuracy is a widely used evaluation metric for classification models, as it measures the proportion of correct predictions made by the model compared to the total number of predictions. This metric is particularly intuitive and straightforward, making it easily interpretable for various audiences.

In classification tasks, the goal is to assign the correct labels to instances of data, so accuracy serves as a clear indicator of how often the model makes correct predictions. It is calculated using the formula: (Number of correct predictions) / (Total number of predictions).

While other metrics, such as Area Under the Curve, can also be valuable in assessing classification models—especially in cases involving imbalanced datasets—accuracy remains a fundamental metric that provides a foundational understanding of model performance.

Mean squared error and root mean squared error are primarily used for regression tasks and are not applicable in the context of classification evaluation. Therefore, accuracy stands out as the correct choice for this question.