fig13

Figure 13. Performance comparison of 12 feature models in a new strength dataset constructed based on “components + domain knowledge” and analysis of the importance of SHAP values of the features. (A and B) are respectively the training set results and test set results of the 12 fitted machine learning models; (C) The optimal GBRT model fitting graph of the new strength dataset constructed based on “component + domain knowledge”; (D and E) are the bar charts and swarm charts for the importance analysis of the SHAP features of the new strength dataset constructed based on “component + domain knowledge”. SHAP: SHapley Additive exPlanations; GBRT: gradient boosting regression tree; R²: the coefficient of determination; RMSE: root mean square error; XGBoost: eXtreme gradient boosting; AdaBoost: adaptive boosting; LGBM: light gradient boosting machine; ExtraTree: extremely randomized tree; Bagging: bootstrap aggregating; RF: random forest; KNN: K-nearest neighbor; DT: decision tree; SVR: support vector regression; ANN: artificial neural network.