Linh Nguyen
Linh Nguyen
Thank you for the prompt response! However, I'm still not getting the same output from shapiq and XGBClassifier. Here is the code that I used ``` import shapiq explainer_shapiq =...
When I use the `shap` package, I could get the accurate prediction ``` import shap explainer_shap = shap.TreeExplainer( model=model, #max_order=1, index="SV" ) sv = explainer_shap(X=X.iloc[0].to_numpy().reshape(1, -1)) prediction = model.predict(X.iloc[0].to_numpy().reshape(1, -1),...
Hi Max, I'm running the same example you have but for binary case ``` from sklearn.datasets import make_classification, make_regression import numpy as np import xgboost import copy def background_clf_dataset() ->...
It's `xgboost 2.1.4` and `shapiq 1.2.3`. Thank you!
Yes, it's hard to reliably reproduce the bug, but I could send you a pickle file of a trained XGBoost model that has trees with no split.
I'm interested in this task as well