Exploring the Relationship Between Feature Attribution Methods and Model Performance
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913359596617728 |
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| author | Silva, Priscylla Silva, Claudio T. Nonato, Luis Gustavo |
| author_facet | Silva, Priscylla Silva, Claudio T. Nonato, Luis Gustavo |
| contents | Machine learning and deep learning models are pivotal in educational contexts, particularly in predicting student success. Despite their widespread application, a significant gap persists in comprehending the factors influencing these models' predictions, especially in explainability within education. This work addresses this gap by employing nine distinct explanation methods and conducting a comprehensive analysis to explore the correlation between the agreement among these methods in generating explanations and the predictive model's performance. Applying Spearman's correlation, our findings reveal a very strong correlation between the model's performance and the agreement level observed among the explanation methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_13957 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring the Relationship Between Feature Attribution Methods and Model Performance Silva, Priscylla Silva, Claudio T. Nonato, Luis Gustavo Machine Learning Machine learning and deep learning models are pivotal in educational contexts, particularly in predicting student success. Despite their widespread application, a significant gap persists in comprehending the factors influencing these models' predictions, especially in explainability within education. This work addresses this gap by employing nine distinct explanation methods and conducting a comprehensive analysis to explore the correlation between the agreement among these methods in generating explanations and the predictive model's performance. Applying Spearman's correlation, our findings reveal a very strong correlation between the model's performance and the agreement level observed among the explanation methods. |
| title | Exploring the Relationship Between Feature Attribution Methods and Model Performance |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.13957 |