This research implements an optimized Light Gradient Boosting Machine (LightGBM) model, highlighting its augmented predictive capabilities, realized through the astute use of Bayesian Optimization for hyperparameter tuning on the FP4026 research data set, and illuminating its adaptability and efficiency in ...
Finally, the Bayesian optimization algorithm is used to perform global hyperparametes tuning for the characteristics of LightGBM with multiple hyperparameters. The experimental results show that compared with support vector regression (SVR), back propagation (BP), gradient boosting decision tree (GBDT),...
The proposed LightGBM model outperforms other methods and offers computational efficiency, making it a valuable tool for risk prediction in supply chain management. Keywords: machine learning; supply chain management; backorder risk; prediction; resilience; light gradient boosting machine; Bayesian ...