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iXGB :
iXGB : improving the interpretability of XGBoost using decision rules and counterfactuals
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- Islam, Mir Riyanul, Doctoral Student, 1991- (författare)
- Mälardalens universitet,Akademin för innovation, design och teknik
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- Ahmed, Mobyen Uddin, Dr, 1976- (författare)
- Mälardalens universitet,Inbyggda system
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- Begum, Shahina, 1977- (författare)
- Mälardalens universitet,Inbyggda system
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(creator_code:org_t)
- 2024
- 2024
- Engelska.
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Ingår i: Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART. - 9789897586804 ; , s. 1345-1353
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Abstract
Ämnesord
Stäng
- Tree-ensemble models, such as Extreme Gradient Boosting (XGBoost), are renowned Machine Learning models which have higher prediction accuracy compared to traditional tree-based models. This higher accuracy, however, comes at the cost of reduced interpretability. Also, the decision path or prediction rule of XGBoost is not explicit like the tree-based models. This paper proposes the iXGB--interpretable XGBoost, an approach to improve the interpretability of XGBoost. iXGB approximates a set of rules from the internal structure of XGBoost and the characteristics of the data. In addition, iXGB generates a set of counterfactuals from the neighbourhood of the test instances to support the understanding of the end-users on their operational relevance. The performance of iXGB in generating rule sets is evaluated with experiments on real and benchmark datasets which demonstrated reasonable interpretability. The evaluation result also supports that the interpretability of XGBoost can be improved without using surrogate methods.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Counterfactuals
- Explainability
- Explainable Artificial Intelligence
- Interpretability
- Regression
- Rule-based Explanation
- XGBoost.
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