"Normalized Absolute Error"
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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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