Name a common metric used to evaluate regression models.

Mean Absolute Error (MAE) is a widely used metric for evaluating regression models because it measures the average magnitude of errors in a set of predictions, without considering their direction. Specifically, it calculates the average of the absolute differences between predicted values and actual values, providing a clear understanding of how well the model's predictions align with actual outcomes. This metric is particularly useful because it provides a straightforward interpretation of the error in the same units as the target variable, making it easier for practitioners to gauge model performance in a real-world context.

In contrast, the other options are primarily employed for classification tasks rather than regression. The accuracy rate, for instance, assesses the proportion of correct predictions in classification problems, while the F1 score combines precision and recall into a single metric for binary classifications. The AUC-ROC metric evaluates the performance of a binary classifier by measuring the area under the receiver operating characteristic curve, again, it is not suitable for regression model evaluation. Therefore, MAE stands out as the appropriate choice for regression model assessment.