3.3.2 Model Development and Evaluation
In this study, four machine learning algorithms were used to establish the meta-model, and the features of the four meta-models were fitted by using the Stacking algorithm. After hyperparameter optimization, the RMSE values of the obtained four meta-models are all lower than 0.32, and the prediction ability of the models is good. This study uses four machine learning algorithms to establish the basic model, and uses the Stacking algorithm to fit the characteristics of these four basic models. After optimization of hyperparameters, the RMSE values of the four models were all lower than 0.43, and the prediction ability of the models was good (Figure 9A). These models are then fitted using the stacking model and regularized. The results show that when thepenalty parameter is 0.0173, the mixture value is 1, that is, the meta-model is completely used for prediction under thispenalty parameter value, and the corresponding RMSE is 0.208, and R2 is 0.954, indicating that the model prediction performance is good (Figure 9B). At the same time, theRMSE values of the meta-model in the training set and test set are 0.193 and 0.289, the R2 is 0.962 and 0,928, and the MAE is 0.068 and 0.116, respectively, and the model performance is good (Figure 9C, Figure 9D).