Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
There are large differences between SHAP values calculated using CPU and GPU for XGBoost models with feature_perturbation='interventional' and model_output='log_loss'. The detailed description of the bug including how to reproduce it and the traceback are provided in https://github.com/shap/shap/issues/3655. The issue seems to be originating at the shap implementation of the xgboost resposiory as mentioned in that issue. It would be great if this can be fixed.
trivialfis
commented
2 days ago
There are large differences between SHAP values calculated using CPU and GPU for XGBoost models with
feature_perturbation='interventional'andmodel_output='log_loss'. The detailed description of the bug including how to reproduce it and the traceback are provided in https://github.com/shap/shap/issues/3655. The issue seems to be originating at the shap implementation of the xgboost resposiory as mentioned in that issue. It would be great if this can be fixed.