A data scientist is building a new model for an ecommerce company.

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.