BookmarkSubscribeRSS Feed
🔒 This topic is solved and locked. Need further help from the community? Please sign in and ask a new question.
ManOfHonor
Obsidian | Level 7

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. 

 

1 ACCEPTED SOLUTION

Accepted Solutions
WendyCzika
SAS Employee

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.

View solution in original post

4 REPLIES 4
WendyCzika
SAS Employee

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.

ManOfHonor
Obsidian | Level 7

now gini equals 0.47 / 0.46.

 

Thanks a lot! 

ManOfHonor
Obsidian | Level 7

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?

WendyCzika
SAS Employee

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.

hackathon24-white-horiz.png

The 2025 SAS Hackathon has begun!

It's finally time to hack! Remember to visit the SAS Hacker's Hub regularly for news and updates.

Latest Updates

How to choose a machine learning algorithm

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.

Discussion stats
  • 4 replies
  • 2895 views
  • 0 likes
  • 2 in conversation