Adding XGBoost, LightGBM & CatBoost modeling in JMP 'decision tree' menu

[Steve_Kim]

As you know, many of data scientist use XGBoost, LightGBM, and CatBoost (gradient boosting decision tree) to solve their problems by using Python.

Could you add these modelings in the decision tree menu (JMP) as well?

If it can be provided by JMP Script Language (JSL), it will be great as well. Thank you. : )

Reference: https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-tree.pdf

[Amir_H]

Please add Extreme Gradient Boosting as mentioned in the above comment as well as Adaptive Boosting (AdaBoost). These 2 methods are very more accurate compared to most other techniques.

[SamGardner]

XGBoost can now be utilized in JMP, if you download the XGBoost Addin from the File Exchange:  Betreff: XGBoost Add-In for JMP Pro 

[Steve_Kim]

Thank you for the amazing update!:)
I tested XGboost addin, and it works well~! (Impressive for the value-added functionality !)
As you know, XGboost is a level-wise tree growth and MS LightGBM is a leaf-wise tree growth.
I hope JMP can add a leaf-wise growth method option someday.

[Sarah-Sylvestre]

Hi @Steve_Kim, thank you for your suggestion! We have captured your request and will take it under consideration.