Exploring the relationship between feature attribution methods and model performance (2024)
- Authors:
- Autor USP: NONATO, LUIS GUSTAVO - ICMC
- Unidade: ICMC
- Subjects: APRENDIZADO COMPUTACIONAL; APRENDIZAGEM PROFUNDA; ANÁLISE DE DESEMPENHO; EDUCAÇÃO
- Keywords: Educational Predictions; Student Success; Explanation Methods; Feature Importance; Correlation Analysis
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Proceedings of Machine Learning Research
- ISSN: 2640-3498
- Volume/Número/Paginação/Ano: v. 257, p. 29-38, 2024
- Conference titles: Annual AAAI Conference on Artificial Intelligence
-
ABNT
SILVA, Priscylla e SILVA, Claudio e NONATO, Luis Gustavo. Exploring the relationship between feature attribution methods and model performance. Proceedings of Machine Learning Research. Vancouver: AAAI. Disponível em: https://proceedings.mlr.press/v257/silva24a.html. Acesso em: 25 fev. 2026. , 2024 -
APA
Silva, P., Silva, C., & Nonato, L. G. (2024). Exploring the relationship between feature attribution methods and model performance. Proceedings of Machine Learning Research. Vancouver: AAAI. Recuperado de https://proceedings.mlr.press/v257/silva24a.html -
NLM
Silva P, Silva C, Nonato LG. Exploring the relationship between feature attribution methods and model performance [Internet]. Proceedings of Machine Learning Research. 2024 ; 257 29-38.[citado 2026 fev. 25 ] Available from: https://proceedings.mlr.press/v257/silva24a.html -
Vancouver
Silva P, Silva C, Nonato LG. Exploring the relationship between feature attribution methods and model performance [Internet]. Proceedings of Machine Learning Research. 2024 ; 257 29-38.[citado 2026 fev. 25 ] Available from: https://proceedings.mlr.press/v257/silva24a.html - Facing the high-dimensions: inverse projection with radial basis functions
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