- What are the shortcomings of the Mean Absolute Percentage Error (MAPE)?
The MAPE, as a percentage, only makes sense for values where divisions and ratios make sense It doesn't make sense to calculate percentages of temperatures, for instance, so you shouldn't use the MAPE to calculate the accuracy of a temperature forecast
- machine learning - Best way to optimize MAPE - Cross Validated
Best way to optimize MAPE Ask Question Asked 9 years, 6 months ago Modified 9 years, 6 months ago
- The difference between MSE and MAPE - Cross Validated
The difference between MSE and MAPE Ask Question Asked 14 years, 6 months ago Modified 8 years, 8 months ago
- forecasting - ARIMA: How to interpret MAPE? - Cross Validated
I interpreted the MAPE like, "on average, the forecast if off by 14%", which sounds fine for me But on the other side the MASE is greater than 1, which means the model is worse than a naive model
- MAPE vs R-squared in regression models - Cross Validated
MAPE vs R-squared in regression models Ask Question Asked 7 years, 10 months ago Modified 7 years, 6 months ago
- Alternative to mean absolute percentage error (MAPE)
MAPE metric has problems when the actual value to be predicted is very small In the extreme when the actual value is 0 then MAPE will be infinity (if the prediction is not exactly 0) What about t
- RMSE or MAPE? which one to choose for accuracy?
If you truly want to find a MAPE-optimal forecast, you should also use the MAPE to fit your model I am not aware of any off-the-shelf forecasting software that does this (if you use an ML pipeline, you may be able to specify any fitting criterion and choose the MAPE), and I have major doubts as to the usefulness of a MAPE-minimal forecast
- MAPE is better but MAE is worse in regression models
I am working on a regression problem to predict price of the vehicle based on its features I have been experimenting with several trials but in one of them, MAPE (Mean Absolute Percentage Error) is
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