groshanlal
commented
2 months ago Can you give some more details about your use case:
- Is your XGBoost model trained for Binary Classification?
- Does the notebook in the README work for you with your version of XGBoost?
Using xgboost 2.0.3, I found the following error: (with categorical support) model_explainer = ModelExplainer( File "/mllab/miniconda3/envs/llm-3.9/lib/python3.9/site-packages/te2rules/explainer.py", line 110, in init self.random_forest = XgboostXGBClassifierAdapter( File "/mllab/miniconda3/envs/llm-3.9/lib/python3.9/site-packages/te2rules/adapter.py", line 254, in init self.random_forest = self._convert() File "/mllab/miniconda3/envs/llm-3.9/lib/python3.9/site-packages/te2rules/adapter.py", line 290, in _convert node = self._build_tree(tree_dict) File "/mllab/miniconda3/envs/llm-3.9/lib/python3.9/site-packages/te2rules/adapter.py", line 266, in _build_tree i = int(tree_dict["split"][1:]) ValueError: invalid literal for int() with base 10