Result of Auto Model

Hi @sara20,

I think it is because in the first case, you have a multi-class classification problem and in the second case you have a binary classification problem (where you can calculate, in addition of the classification error and the accuracy,  the AUC, sensitivity etc .)

Regards,

Lionel 

@lionelderkrikor

Hello 

Thank you for the answer but I have two datasets which they are the same. Both of them are numeric but it is really strange for the result and metrics( both of them are very normal datas). So what is the reason for that?

@sara20

Can you specify the type of your label in both cases ?

Regards,

Lionel

@lionelderkrikor

Both of them have Nominal labels. َAlso yesterday I up date my RM to version 9.7. In version 9.6 I didn't have this problem. Is it the problem for version 9.7? If yes so how can I change it to version 9.6? or salve the problem?

Thank you

@sara20

In order we can reproduce what you observe, can you share : 

 - the two datasets
 - and specify in both cases the label

Regards,

Lionel

@lionelderkrikor

Thank you very much for following the problem but unfortunately the data is not mine and I can not share it. Can I change the process in Auto Model in order to fix the problem?

Even if both labels are nominal type, if in one dataset there are only two values of the label it will be treated as binominal and thus as @lionelderkrikor explained you will have additional metrics available like AUC.  But if there are more then two values of the label, it will be treated as polynominal and then you will have only accuracy.  You can always check this by taking the output of Auto Model and opening the underlying process and then checking the data type and the data values for your label.