"Normalized Absolute Error"
islem_h
New Altair Community Member
Hi everyone 
I applied different models (Random Forest, SVR, GLM...) to make predictions of a numerical target variable. In order to assess their performances, I chose RMSE and NAE (Normalized Absolute Error) from the options suggested by RapidMiner's operator "Performance".
I got (slightly) different rankings using the 2 metrics. What could be a reason for that?
It would be very helpful if anyone could provide me with a reference (documentation) about the Normalized Absolute Error (NAE). I couldn't interpret the results (1,3 and 0,62 and 0,59) based only on Rapidminer's definition of NAE.
Thank you a lot in advance!
I applied different models (Random Forest, SVR, GLM...) to make predictions of a numerical target variable. In order to assess their performances, I chose RMSE and NAE (Normalized Absolute Error) from the options suggested by RapidMiner's operator "Performance".
I got (slightly) different rankings using the 2 metrics. What could be a reason for that?
It would be very helpful if anyone could provide me with a reference (documentation) about the Normalized Absolute Error (NAE). I couldn't interpret the results (1,3 and 0,62 and 0,59) based only on Rapidminer's definition of NAE.
Thank you a lot in advance!
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Answers
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Hi @islem_h
Can you go through the below link where these performance metrics are explained with an example? There can be slight variation as in NAE we are normalizing which depends on minimum and maximum values. I think the better comparison would be with Normalized RMSE(not sure RM has this) and NAE. If possible please try to post the dataset and XML process so that we can understand better and inform you.
https://www.researchgate.net/publication/327646833_Quality_of_the_predictions_mean_absolute_error_accuracy_and_coverage
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Sorry guys, a bit short on time today but maybe the source helps here:The class doc states:Hope this helps,
"Normalized absolute error is the total absolute error normalized by the error simply predicting the average of the actual values."
Ingo
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