2024-08-09-silva24a.md

title	booktitle	year	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Exploring the Relationship Between Feature Attribution Methods and Model Performance	Proceedings of the 2024 AAAI Conference on Artificial Intelligence	2024	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 level of agreement observed among the explanation methods.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	silva24a	0	Exploring the Relationship Between Feature Attribution Methods and Model Performance	29	38	29-38	29	false	Silva, Priscylla and Silva, Claudio and Nonato, Luis Gustavo	given family Priscylla Silva given family Claudio Silva given family Luis Gustavo Nonato	2024-08-09		Proceedings of the 2024 AAAI Conference on Artificial Intelligence	257	inproceedings	date-parts 2024 8 9	https://raw.githubusercontent.com/mlresearch/v257/main/assets/silva24a/silva24a.pdf