Preserve generalization power while reducing run-time.
Nice work again, Avi!
Christoph Molnar also wrote about it in his interpretable machine learning model book, it is called "global surrogate model": https://christophm.github.io/interpretable-ml-book/global.html
This is a great idea! I'm curious how mathmatically sound this would be to create a "global surrogate model" for xgboost or other boosted trees models?
Intuitively it seems very similar.
Nice work again, Avi!
Christoph Molnar also wrote about it in his interpretable machine learning model book, it is called "global surrogate model": https://christophm.github.io/interpretable-ml-book/global.html
This is a great idea! I'm curious how mathmatically sound this would be to create a "global surrogate model" for xgboost or other boosted trees models?
Intuitively it seems very similar.