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christophM / interpretable-ml-book

Book about interpretable machine learning
https://christophm.github.io/interpretable-ml-book/
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Model Specific Methods - xgboost #123

Closed lcrmorin closed 3 years ago

lcrmorin commented 5 years ago

I see there is now a model-specific methods section, starting with Neural Networks. You might be interested in some advances in xgboost - and more generally trees - explainability. Here is a medium post that explain the method (I am not sure it is new or even meaningfull for small trees). However I have found the publication of the R xgboostExplainer package of interest, and quite usefull to use given the general performance of xgboost models. I am particularly fond of the waterfall diagrams to translate the variable impacts in log-odds.

christophM commented 5 years ago

Thanks for the suggestion and the link! Indeed I am planning to write something for random forests and boosted tree ensembles such as xgboost.

christophM commented 3 years ago

Thanks for that suggestion (I know it was 1.5 years ago =)). For a long time I was not sure about the scope of the book. But after this long time I decided to only include model-agnostic techniques, simple interpretable models and some deep learning specific stuff.