fig4

Figure 4. XGBoost algorithm for the ML model. (A) The general architecture of XGBoost algorithm, where f_i (i  =  1, 2, …, n) is the sub-output corresponding to each decision tree. The latter tree model is a correction of prediction errors of the previous models; (B) The repeated 10-fold cross validation (Reprinted from Ref.^[49], Copyright of © 2025 Wiley‐VCH GmbH). XGBoost: Extreme gradient boosting; ML: machine learning.