Comments (1)
Thanks for spotting this!
By definition, both SHAP tensors obtained for a binary classifier should add up to 0.0 for each observation and feature.
Your example provides evidence that totals may deviate by as much as as 0.01 due to imprecisions in the SHAP explainer's approach for estimating SHAP values.
This is now addressed by PR #24.
The fix is not to raise an exception if the totals are not 0.0, but to log a warning instead, stating the range of observed totals. As long as these totals are small (e.g., less than 0.05, corresponding to 5%pt probability), it should be safe to ignore these warnings.
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Related Issues (20)
- Add a UnivariateTargetSimulator
- Expose full distribution of outputs on simulation results
- Mismatch of feature ordering (matrices vs. dendograms) HOT 1
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- Run times are huge HOT 9
- ModuleNotFoundError: No module named 'facet.data'; 'facet' is not a package HOT 2
- gamma-facet==1.0.1 not compatible with latest shap==0.38.1 HOT 2
- Future Implementation for Tensorflow and Pytorch HOT 2
- SHAP Feature Values Inverted HOT 4
- understanding synergy asymmetry HOT 3
- README.rs dataset load can be automated for users HOT 1
- cannot import LearnerInspector etc HOT 1
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- Isolated Sphinx doc does not build due to missing pytools script HOT 3
- Versioning & Compatibility XGBoost HOT 1
- Support for scikit-learn models HOT 3
- Support SAGE values similar to SHAP
- How to calculate SRI for nonlinear models?
- Add methods to model inspector to return SHAP values and associated feature data
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