Test dataset (example set) not equal to sum of confusion matrix tabs.
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@ssv112,
A hypothesis :
If you are using Auto-Model, Auto-Model first calculates the performance on 7- hold out sets of the test set. Then Auto-Model uses the operator called Performance Average (Robust) to create the final confusion matrix.
This operator removes the performances with the highest and the lowest value for the main criterion from the input and calculates the average of the rest.
So there is a factor 5/7 (or 7/5) between the number of samples in the initial test set and the sum of test samples in the final confusion matrix.
Hope this helps.... if not please share your data and process.
Regards,
Lionel
A hypothesis :
If you are using Auto-Model, Auto-Model first calculates the performance on 7- hold out sets of the test set. Then Auto-Model uses the operator called Performance Average (Robust) to create the final confusion matrix.
This operator removes the performances with the highest and the lowest value for the main criterion from the input and calculates the average of the rest.
So there is a factor 5/7 (or 7/5) between the number of samples in the initial test set and the sum of test samples in the final confusion matrix.
Hope this helps.... if not please share your data and process.
Regards,
Lionel
Sort by:
1 - 1 of
11
New Altair Community Member
Accepted Answer
@ssv112,
A hypothesis :
If you are using Auto-Model, Auto-Model first calculates the performance on 7- hold out sets of the test set. Then Auto-Model uses the operator called Performance Average (Robust) to create the final confusion matrix.
This operator removes the performances with the highest and the lowest value for the main criterion from the input and calculates the average of the rest.
So there is a factor 5/7 (or 7/5) between the number of samples in the initial test set and the sum of test samples in the final confusion matrix.
Hope this helps.... if not please share your data and process.
Regards,
Lionel
A hypothesis :
If you are using Auto-Model, Auto-Model first calculates the performance on 7- hold out sets of the test set. Then Auto-Model uses the operator called Performance Average (Robust) to create the final confusion matrix.
This operator removes the performances with the highest and the lowest value for the main criterion from the input and calculates the average of the rest.
So there is a factor 5/7 (or 7/5) between the number of samples in the initial test set and the sum of test samples in the final confusion matrix.
Hope this helps.... if not please share your data and process.
Regards,
Lionel
In order we can reproduce what you observe, can you share :
- your process
- your data
Thanks,
Regards,
Lionel