iXGB: Improving the Interpretability of XGBoost Using Decision Rules and Counterfactuals Article Swipe
Mir Riyanul Islam
,
Mobyen Uddin Ahmed
,
Shahina Begum
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5220/0012474000003636
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5220/0012474000003636
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.
Related Topics
Concepts
Interpretability
Decision tree
Artificial intelligence
Computer science
Machine learning
Benchmark (surveying)
Gradient boosting
Boosting (machine learning)
Data mining
Counterfactual conditional
Tree (set theory)
Set (abstract data type)
Mathematics
Random forest
Counterfactual thinking
Epistemology
Philosophy
Mathematical analysis
Geography
Geodesy
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5220/0012474000003636
- OA Status
- gold
- Cited By
- 1
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392305522
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4392305522Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5220/0012474000003636Digital Object Identifier
- Title
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iXGB: Improving the Interpretability of XGBoost Using Decision Rules and CounterfactualsWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Mir Riyanul Islam, Mobyen Uddin Ahmed, Shahina BegumList of authors in order
- Landing page
-
https://doi.org/10.5220/0012474000003636Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5220/0012474000003636Direct OA link when available
- Concepts
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Interpretability, Decision tree, Artificial intelligence, Computer science, Machine learning, Benchmark (surveying), Gradient boosting, Boosting (machine learning), Data mining, Counterfactual conditional, Tree (set theory), Set (abstract data type), Mathematics, Random forest, Counterfactual thinking, Epistemology, Philosophy, Mathematical analysis, Geography, Geodesy, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2024: 1Per-year citation counts (last 5 years)
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19Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.is_in_top_10_percent | False |