Exploring the Relationship Between Feature Attribution Methods and Model Performance
<|reference_start|>Exploring the Relationship Between Feature Attribution Methods and Model Performance: 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.<|reference_end|>
@article{silva2024exploring,
    title={Exploring the Relationship Between Feature Attribution Methods and Model
  Performance},
    author={Priscylla Silva, Claudio T. Silva, Luis Gustavo Nonato},
    journal={arXiv preprint arXiv:2405.13957},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13957},
    primaryClass={cs.LG}
}