Is there a way we can tweak the GBM in sas EM to implement extreme gradient boosting algorithm?
Further, what is the best way to control overfitting in GBM using EM?
It could be done using the Open Source Integration Node. Extreme Gradient Boosting is not something available from SAS, currently. It is certainly something I hope they add in the near future though.
EDIT:
I feel like pointing out here that the main appeal of XGBoost is it's performance from an engineering standpoint, rather than statistical. This is what really sets this pacakage apart from the GBM from SAS.
As far as overfitting is concerned, two traditional methods would be reducing the number of iterations as well as adjusting your subsample size.
There are pakages in R to do this, not sure about Python
If you have IML there is a interface to R, also WPS has an interface.
You might check xgboost.
I am out of my comfort zone on this reply
xgboost: eXtreme Gradient Boosting
However, I would switch to SAS when it is available, as long as SAS nakes it part of stat. The R packages are the wild west of programming and this is not
like the Atkinson and Whittaker functions in previous posts. You can examine the R source code.
SAS Viya includes a distributed gradiant boosting proc which implements a very similar algorihm to xgboost
Hello Experts,
Do you have an example of how running a R code with Extreme Gradient Boosting (xgboost) is SAS EMiner using the Open Source Integration node?
Thanks a lot!
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Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
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