What are some common metrics used for evaluating the performance of regression models?

Common metrics for evaluating regression models include Mean Absolute Error (MAE), which measures the average magnitude of errors in a set of predictions; Mean Squared Error (MSE), which squares the errors before averaging, emphasizing larger errors; and R-squared, which indicates the proportion of variance in the dependent variable explained by the model. Each metric provides different insights into model performance and helps in selecting the best model for a given application.