SAS Enterprise Miner - Decision Tree and Gradient Boosting Run issue

jcpenny2002 — Wed, 15 May 2013 17:49:26 GMT

Hello, everyone

Recently I started using SAS EM and applied Decision Tree/Gradient Boosting model to my data. The data's response rate is 1%. Anyhow, for some reason, the Decision Tree run was suspended for Run time error and Gradient Boosting run results shows no lift, just ramdom distribution of data.

After I oversampled the data and make the new sample's response rate to be 10% or more, the decision tree and gradient boosting run were successful.

My question is, is there any parameter in Decision Tree and Gradient Boosting, after I change it, they can run successfully when the sample data's response rate is small, say 1% or below? Thanks,

Jimmy

Re: SAS Enterprise Miner - Decision Tree and Gradient Boosting Run issue

DougWielenga — Wed, 16 Aug 2017 18:22:10 GMT

Your question is a common one and is discussed in part in SAS Note 47965 available at

http://support.sas.com/kb/47/965.html

In general, it is always good to check running a Tree model if your Gradient Boosting node is not running since Gradient Boosting models. The strategies described in the note above will likely help you with your original data without oversampling.

For Gradient Boosting, try the following:
- lowering the Minimum Categorical Size property to 2 or 3
- changing the Missing Values property

To investigate further, open the Gradient Boosting node results and click on

View --> SAS Results --> Log

to view the log from your Gradient Boosting node and look for notes similar to the following:

...
NOTE: Will not search for split on variable A.
NOTE: Too few acceptable cases.
NOTE: Option MINCATSIZE=5 may apply.
NOTE: Will not search for split on variable B.
NOTE: Too few acceptable cases.
NOTE: Option MINCATSIZE=5 may apply
....

We have seen that message appear when some of the samples had an insufficient number of events and non-events and Gradient Boosting was unable to iterate. Sampling is used at different points to determine split values, and then the model is fit to the whole data set. If there are not enough events, then SAS Enterprise Miner cannot determine where the splits should occur.

Are other models able to run such as a regression model or decision tree? If so, examine your regression results to see whether there are many near-zero standard errors. Some customers have the opposite problem - infinite standard errors. For more information about this problem, please review

Usage Note 22599: Understanding and correcting complete or quasi-complete separation problems
http://support.sas.com/kb/22/599.html

I hope this helps!

Doug

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SAS Enterprise Miner - Decision Tree and Gradient Boosting Run issue

Re: SAS Enterprise Miner - Decision Tree and Gradient Boosting Run issue