Comments (2)
The CEM explainer in the toolkit is not model agnostic and so requires a differentiable model to work. We have a model agnostic version in Watson Openscale (IBM product) which works with arbitrary classifiers.
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Thanks!
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Related Issues (20)
- Defect FeatureBinarizer HOT 1
- Version reconciliation with AIF360
- AIX360 conda version HOT 9
- CEMExplainer scikit-learn compatibility & "predict_long" method error HOT 3
- UnboundLocalError: local variable 'newinnerProduct' referenced before assignment HOT 1
- Feature request: Update Tensorflow version to be compatible with Python 3.8+ HOT 2
- How to execute monotonicity and faithfulness on keras dense layer models?
- How to execute the metrics on regression problems instead of classification? HOT 2
- How to evaluate shap local explanations with Faithfulness and monotoncity metrics?
- ModuleNotFoundError: No module named 'aix360.algorithms.rule_induction' HOT 3
- Ripper rule induction algorithm treats timestamp type features as categorical HOT 1
- Questions about the results obtained by XAI method HOT 1
- ContextualVersionConflict
- Error: "elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison"
- Upgrade the tensorflow version in setup.py to support higher versions of Python HOT 4
- ModuleNotFoundError: No module named 'tensorflow.contrib' HOT 26
- aif360.mybluemix.net does not exist HOT 1
- aix360.mybluemix.net HOT 2
- Update the outdated project website URL HOT 1
- MEPSDataset preprocessing return empty df. HOT 1
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