XGBoost and LightGBM implementations

Hi @Nirr3 - I'm going to connect you with @bhupendra_patil. He has a pilot project that is working on exactly this kind of idea.

Scott

Hi @sgenzer and @bhupendra_patil,

Is there any news about this? I would also be interested in trying a RapidMiner XGBoost implementation and see how it compares with an existing model based on H2O Gradient Boosted Trees.

Thanks

Try the Smile extension. I have had very good results with it, especially the GBT.

Xgboost is available for Java, including its GPU support. 

I have also noticed that the current version of H2O provides not only GBM (I assume this model is available in RM) but also XGBoost interface (yes, the original one as well as its light version), with GPU support which gives fantastic training acceleration but which restricts the tree learning options, so I found out it is not producing models of the accuracy as good as that of the CPU based backend.

If yes, then this is the problem: "Windows not supported in the JVM package"

@mschmitz Yes, this was the one, also announced on the main web site. Pity, looks like a short wait for Windows to be supported?

Its like this since 2016 if i remember correctly. They have no interest in supporting Windows.

I've done some more digging and I found out this French fork of XGBoost. If anybody wanted to do a DIY xgboost extension for RM (which seems to include Windows), here is a link to try:

•  https://github.com/criteo-forks/xgboost-jars/.

It is becoming a sort of second-hand development and I am not sure if GPU would be supported in this project.