In the work, we mainly utilized the ensemble learning strategy to construct a comprehensive model by combining base learners. Compared to the single machine learning, ensemble learning not only produces a more stable global model, but also guarantees diminishing uncertainty. Herein, LightGBM, a recent modification of the gradient boosting (GB) algorithm , is considered. It improves the efficiency and scalability of the GB algorithm without sacrificing its inherited effective performance. This approach results in a faster and less resource-intensive implementation of gradient boosting, suitable for frequent retraining and rapid evaluation of high-dimensional datasets. To further evaluate the performance of the LightGBM algorithm, it was compared with six classic ML algorithms, including Support Vector Machine (SVM), partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), Kernel Ridge Regression (KRR), k-Nearest Neighbors (kNN), Random Forest (RF).