I occupy myself with credit scoring, I use Enterprise Miner.
How to prevent overfitting when training scorecard model ?
I detect this phenomena by comparing train and validation gini coefficients. Mine are 0.53 and 0.43 respectively.
In the Scorecard node, you can change the Selection Model property to anything but "None", then for the Criterion property, use either Validation Error or Validation Misclassification to help with overfitting of the training data.
In the Scorecard node, you can change the Selection Model property to anything but "None", then for the Criterion property, use either Validation Error or Validation Misclassification to help with overfitting of the training data.
now gini equals 0.47 / 0.46.
Thanks a lot!
Your advice worked pretty well but now I have to few variables. The number changed from 10 to 5.
What can I do to prevent this baheavior?
You can try adjusting the other properties in the Model Selection section to relax the criterion for including effects in the model, or you can force certain effects into the model.
SAS Innovate 2025 is scheduled for May 6-9 in Orlando, FL. Sign up to be first to learn about the agenda and registration!
Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
Find more tutorials on the SAS Users YouTube channel.