Interpretable machine learning for predicting compression index of clays using SHAP and gradient boosting models
Khaled Hamdaoui, Ali Benzaamia, Billal Sari Ahmed, Mohamed Elhebib Guellil et al. • Journal of Engineering and Applied Science
(2025)
Abstract This study introduces a novel, interpretable machine learning framework for predicting the compression index (Cc) of clay soils by integrating three advanced gradient boosting algorithms—XGBoost, CatBoost, and LightGBM—with SHapley...
DOAJ
Open Access (DOAJ)
DOI:
10.1186/s44147-025-00727-4