The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
Why is the Confusion matrix generated by Performance Vector and the Predictive model is different?
Contributor II am using Gradient Boosted Trees for my dataset. The process output shows the Performance Vector which gives the accuracy and confusion matrix, and Gradient Boosted Model which gives the model metrics, Confusion matrix, Variable importance, model summary and scoring history. But both the confusion matrix are different. Which should confusion matrix should I consider to evaluate my model?
Tagged:
0
Best Answer
-
MartinLiebig
Administrator, Moderator, Employee-RapidMiner, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,533 
RM Data Scientist
Hi,
the confusion matrix in the model are _training_ errors. So you should usually work on the Performance Vector, not the Gradient Boosted Model values. These are sometimes interesting to have a look on overfitting (e.g. add more complexity or not).
~Martin
- Sr. Director Data Solutions, Altair RapidMiner -
Dortmund, Germany2
Answers
> These are sometimes interesting to have a look on overfitting (e.g. add more complexity or not).
True, but in general I am in the school of "just forget training errors completely". They create more damage than anything else :smileywink:
Have a nice weekend,
Ingo