Formally Explaining Decision Tree Models with Answer Set Programming
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917188011556864 |
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| author | Takemura, Akihiro Otani, Masayuki Inoue, Katsumi |
| author_facet | Takemura, Akihiro Otani, Masayuki Inoue, Katsumi |
| contents | Decision tree models, including random forests and gradient-boosted decision trees, are widely used in machine learning due to their high predictive performance. However, their complex structures often make them difficult to interpret, especially in safety-critical applications where model decisions require formal justification. Recent work has demonstrated that logical and abductive explanations can be derived through automated reasoning techniques. In this paper, we propose a method for generating various types of explanations, namely, sufficient, contrastive, majority, and tree-specific explanations, using Answer Set Programming (ASP). Compared to SAT-based approaches, our ASP-based method offers greater flexibility in encoding user preferences and supports enumeration of all possible explanations. We empirically evaluate the approach on a diverse set of datasets and demonstrate its effectiveness and limitations compared to existing methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_03845 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Formally Explaining Decision Tree Models with Answer Set Programming Takemura, Akihiro Otani, Masayuki Inoue, Katsumi Artificial Intelligence Logic in Computer Science Decision tree models, including random forests and gradient-boosted decision trees, are widely used in machine learning due to their high predictive performance. However, their complex structures often make them difficult to interpret, especially in safety-critical applications where model decisions require formal justification. Recent work has demonstrated that logical and abductive explanations can be derived through automated reasoning techniques. In this paper, we propose a method for generating various types of explanations, namely, sufficient, contrastive, majority, and tree-specific explanations, using Answer Set Programming (ASP). Compared to SAT-based approaches, our ASP-based method offers greater flexibility in encoding user preferences and supports enumeration of all possible explanations. We empirically evaluate the approach on a diverse set of datasets and demonstrate its effectiveness and limitations compared to existing methods. |
| title | Formally Explaining Decision Tree Models with Answer Set Programming |
| topic | Artificial Intelligence Logic in Computer Science |
| url | https://arxiv.org/abs/2601.03845 |