Add SAGE integration and bump version to 1.0.0#85
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monte-flora
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Apr 1, 2026
monte-flora
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- Add ExplainToolkit.sage() method for SAGE global feature importance (Covert et al. 2020). Supports individual and grouped SAGE via sage.MarginalImputer and sage.GroupedMarginalImputer.
- Add sage-importance as optional dependency (pip install scikit-explain[sage])
- Add group_sage() utility for post-hoc grouping of SAGE values
- Export group_local_values, group_feature_values, to_skexplain_importance, group_sage from skexplain top-level for advanced workflows
- Fix to_skexplain_importance to handle grouped_sage method name
- Fix group_local_values to handle list-type attrs (method, estimator name)
- Add "grouped_sage" to PlotImportance SINGLE_VAR_METHODS and DISPLAY_NAMES_DICT
- Add tutorial notebook: 13_sage_and_global_ranking.ipynb (SAGE computation, SHAP+SAGE side-by-side, grouped importance)
- Bump version to 1.0.0
- Add ExplainToolkit.sage() method for SAGE global feature importance (Covert et al. 2020). Supports individual and grouped SAGE via sage.MarginalImputer and sage.GroupedMarginalImputer. - Add sage-importance as optional dependency (pip install scikit-explain[sage]) - Add group_sage() utility for post-hoc grouping of SAGE values - Export group_local_values, group_feature_values, to_skexplain_importance, group_sage from skexplain top-level for advanced workflows - Fix to_skexplain_importance to handle grouped_sage method name - Fix group_local_values to handle list-type attrs (method, estimator name) - Add "grouped_sage" to PlotImportance SINGLE_VAR_METHODS and DISPLAY_NAMES_DICT - Add tutorial notebook: 13_sage_and_global_ranking.ipynb (SAGE computation, SHAP+SAGE side-by-side, grouped importance) - Bump version to 1.0.0 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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