What is the difference between RMSE and MAE?

Can you explain the difference between RMSE and MAE?

Sure! RMSE stands for Root Mean Square Error, while MAE stands for Mean Absolute Error. Both are metrics used to measure the accuracy of a model's predictions.

What are the main differences in how they calculate errors?

RMSE squares the errors before averaging, which gives more weight to larger errors, while MAE takes the absolute values of errors, treating all errors equally.

When should I use RMSE instead of MAE?

Use RMSE when you want to penalize larger errors more heavily, especially in cases where large errors are particularly undesirable. MAE is better for a more straightforward interpretation of average error.

Can you give an example of when each would be more appropriate?

Certainly! RMSE is often used in regression problems where large errors are critical, like in financial forecasting. MAE is useful in scenarios like predicting delivery times, where all errors are equally important.