For regression tasks that predict continuous numerical values, such as estimating delivery times, appropriate evaluation metrics quantify the difference between predicted and actual values. Two commonly used metrics are:​

Mean Squared Error (MSE): Calculates the average of the squares of the errors, providing a measure of the quality of an estimator.​

Root Mean Squared Error (RMSE): The square root of MSE, offering an error metric in the same units as the target variable, which aids in interpretability.​

These metrics are standard for assessing the performance of regression models, especially when the goal is to predict continuous outcomes accurately.