Which metric is commonly used to determine the effectiveness of regression models?

Mean Absolute Error (MAE) is a widely used metric for evaluating regression models because it quantifies the average absolute difference between predicted and actual values. By taking the absolute values of the errors, MAE provides a straightforward interpretation of the model's performance in terms of the average magnitude of errors, without considering their direction. This makes it particularly useful for regression tasks where the goal is often to minimize these differences to improve prediction accuracy.

In contrast, some of the other metrics listed are more applicable to classification tasks. Accuracy measures the proportion of correct predictions among all predictions and is not suitable for regression since it does not provide information about the scale of prediction errors. The F1 score is specifically designed for classification problems, balancing precision and recall, and it is not relevant for continuous outcome predictions typical of regression. Log Loss is also a classification metric that measures the performance of a model whose output is a probability value between 0 and 1, thus making it unsuitable for regression evaluations.

MAE, being focused on the error magnitude, is integral for determining how well regression models perform, making it the appropriate choice for this context.