Explaining the output of machine learning models with more accurately estimated Shapley values
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Updated
Aug 15, 2025 - HTML
Explaining the output of machine learning models with more accurately estimated Shapley values
All about explainable AI, algorithmic fairness and more
Counterfactual SHAP: a framework for counterfactual feature importance
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Code of experiments implemented in the paper "Explainability of Predictive Process Monitoring results: Techniques, Experiments and Lessons Learned", comparing XAI methods at different granularities (global/local) with different settings on predictive process monitoring outcomes using process mining event logs
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Project looking into air traffic trends after winter Holidays in the US.
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