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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2309.17095 |
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| _version_ | 1866908288847708160 |
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| author | Rida, Adam Lesot, Marie-Jeanne Renard, Xavier Marsala, Christophe |
| author_facet | Rida, Adam Lesot, Marie-Jeanne Renard, Xavier Marsala, Christophe |
| contents | Explainable AI (XAI) methods have mostly been built to investigate and shed light on single machine learning models and are not designed to capture and explain differences between multiple models effectively. This paper addresses the challenge of understanding and explaining differences between machine learning models, which is crucial for model selection, monitoring and lifecycle management in real-world applications. We propose DeltaXplainer, a model-agnostic method for generating rule-based explanations describing the differences between two binary classifiers. To assess the effectiveness of DeltaXplainer, we conduct experiments on synthetic and real-world datasets, covering various model comparison scenarios involving different types of concept drift. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_17095 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dynamic Interpretability for Model Comparison via Decision Rules Rida, Adam Lesot, Marie-Jeanne Renard, Xavier Marsala, Christophe Machine Learning Artificial Intelligence Explainable AI (XAI) methods have mostly been built to investigate and shed light on single machine learning models and are not designed to capture and explain differences between multiple models effectively. This paper addresses the challenge of understanding and explaining differences between machine learning models, which is crucial for model selection, monitoring and lifecycle management in real-world applications. We propose DeltaXplainer, a model-agnostic method for generating rule-based explanations describing the differences between two binary classifiers. To assess the effectiveness of DeltaXplainer, we conduct experiments on synthetic and real-world datasets, covering various model comparison scenarios involving different types of concept drift. |
| title | Dynamic Interpretability for Model Comparison via Decision Rules |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2309.17095 |