How do you measure forecast accuracy and compare MAE to RMSE?
This tests out-of-sample validation and how MAE and RMSE weight errors. A strong answer demands a train-test split, defines both, and notes RMSE punishes outliers more while MAE is more robust. A red flag is citing in-sample fit instead of held-out error.
This tests whether you validate on genuinely held-out data and understand the error-sensitivity trade-off between MAE and RMSE. A strong answer insists on a train-test split, because only out-of-sample performance reveals true forecast accuracy. It defines MAE as average absolute error and RMSE as the root of average squared error, noting that squaring amplifies large deviations. Prefer MAE for robust, unit-interpretable accuracy when outliers are routine; prefer RMSE when large errors are disproportionately costly.
Read the original → otexts.com
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- #mae
- #rmse
- #model-evaluation
- #time-series
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