What are the pitfalls of MAPE (Mean Absolute Percentage Error)?
mediumAnswer
- %.
- Issues: (1) undefined when y = 0 and explodes when y is close to 0; (2) asymmetric — over-forecasting is bounded (max 100% error), under-forecasting is unbounded; (3) biases model selection toward under-prediction.
- Alternatives: sMAPE (symmetric), MASE (compare to naive forecast), WAPE (weighted / total absolute error / total absolute y), or use quantile losses.
- Never use MAPE with targets that can be zero or negative.
Check yourself — multiple choice
- MAPE handles zeros gracefully
- MAPE is undefined for y=0, asymmetric, and biases toward under-prediction
- MAPE is symmetric
- MAPE is always the best regression metric
MAPE: undefined at zero, asymmetric penalty ⇒ biases toward under-forecasting.
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