AutoML can use several evaluation metrics for regression problems, including Mean Absolute Error (MAE) which measures the average absolute difference between predicted and actual values, Root Mean Squared Error (RMSE) which penalizes larger errors more heavily, R-squared (R²) which indicates the proportion of variance explained by the model, and Mean Absolute Percentage Error (MAPE) which provides a relative measure of error. Each metric is suitable for different scenarios; for example, MAE is preferred when the dataset contains outliers, while RMSE is useful for comparing the performance of different models.