Comments (1)
Thanks for reporting. Your example is not reproducible, please provide a reproducible example I am afraid otherwise we wont have the capacity to figure out a model and a dataset that reproduces the issue.
Concretely we would need one script that reproduces your error, that means that the model definition, training steps, etc. and the data definition is all done within that script and does not have dependencies to any internal code/data of yours.
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Related Issues (20)
- TypeError: In v0.20, force plot now requires the base value as the first parameter! Try shap.plots.force(explainer.expected_value, shap_values) or for multi-output models try shap.plots.force(explainer.expected_value[0], shap_values[0]). HOT 1
- Key not found with shap.TreeExplainer and XGBRegressor HOT 1
- BUG: Unable to Generate SHAP values for a dataframe containing text data trained on lstm model HOT 2
- BUG: Error with SHAP Partial Dependence Plot: ValueError: DataFrame.dtypes for data must be int, float, bool or category
- ENH: integrated gradients HOT 1
- Support tf 2.16 and keras 3 HOT 4
- BUG: shap.plots.bar(shap_values) TypeError
- BUG: Custom masker offset is not working properly
- BUG: AssertionError, the SHAP explanations do not sum up to the model's output!
- BUG: Background dataset subsampling HOT 4
- BUG: AttributeError: 'tuple' object has no attribute 'as_list' (tensorflow 2.15.0) HOT 2
- [Meta-issue] Release 0.46.0
- [Meta-issue] Increase test coverage
- Unable to generate explanations for predict_proba using treeexplainer HOT 1
- BUG: wrong output information on the web document HOT 2
- BUG: ImportError when using numpy2 HOT 3
- ENH: Possible support of SHAP for exaplaining custom layers constructed from supported layers
- ENH: Add compatibility for Random Forest Quantile Regressor
- Numpy2 Support HOT 4
- BUG: tests/explainers/test_tree.py::TestExplainerXGBoost::test_xgboost_dmatrix_propagation[XGBRFClassifier] failing on macOs
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