## 3 Results
### XGBoost Model Evaluation  
The optimal combination of feature variables can provide a more accurate representation of the input features. Based on the correlation between feature variables and target labels, AC, CAL, CNL, DEN, SP, RLLS, PE, GR, RLLD, and RT were selected as input feature variables. An exhaustive search method was applied to identify all possible combinations. In this paper, the number of logging feature combinations was pre-set to 1, and 10 combinations of feature variables were searched. Subsequently, the combination of feature variables was input into the XGBoost model, and the minimum absolute error (MAE) and maximum coefficient of determination (R\({}^{2}\)) were used as the evaluation indices to optimize the feature combination method. The results of the 1024 feature combination scores are shown in Table 2. The optimal feature combination for the POR model is [AC, CAL, CNL, DEN, SP, RLLS, PE, GR], with a MAE of 0.0075 for the training set and 0.0195 for the test set and an R\({}^{2}\) of 0.97 for the training set and 0.68 for the test set. The optimal feature combination for PERM is [AC, CAL, SP, RLLS, PE, GR, RLLD], the training set MAE is 0.0003, the test set MAE is 0.0006, the training set R\({}^{2}\) is 0.99, and the test set R\({}^{2}\) is 0.31. The optimal feature combination for LOG(PERM) regression is [AC, CAL, CNL, DEN, SP, RLLS, PE, GR, RLLD], the training set MAE is 0.3757, the test set MAE is 0.0920, the training set R\({}^{2}\) is 0.99 and the test set R\({}^{2}\) is 0.31; the optimal feature combination for LOG(PERM) regression is [AC, CAL, CNL, DEN, SP, RLLS, PE, GR, RLLD], the training set MAE is 0.0920, the training set R\({}^{2}\) is 0.97, and the test set R\({}^{2}\) is 0.57. The optimal feature combination for Artcan(PERM) regression is [CAL, CNL, DEN, SP, PE, GR, RLLD], the training set MAE is 0.0932, test set MAE is 0.0244, training set R\({}^{2}\) is 0.96, and test set R\({}^{2}\) is 0.50. The RandomizedSearchCV function was used for large-scale and efficient searches based on random search results. Subsequently, the GridSearchCV function was utilized for more precise searches, ensuring a wide search range and accurate search results while reducing computational model runtime. Additionally, the functions were combined with 5-fold cross-validation to evaluate different parameter configurations with the objective of maximizing the model performance. The main hyper-parameters that must be configured to build the regression prediction model using the XGBoost algorithm are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Feature Combination** & **MAE of** & **MAE of Test** & **R\({}^{2}\) of** & **R\({}^{2}\) of Test** \\  & **Training Set** & **Set** & **Training Set** & **Set** \\ \hline POR & [AC, CAL, CNL, DEN, SP, RLLS, PE, GR] & 0.0075 & 0.0195 & 0.97 & 0.68 \\ PERM & [AC, CAL, SP, RLLS, PE, GR, RLLD] & 0.0003 & 0.0006 & 0.99 & 0.31 \\ LOG(PERM) & [AC, CAL, CNL, DEN, SP, RLLS, PE, GR, RLLD] & 0.3757 & 0.0920 & 0.97 & 0.57 \\ Arctcan(PERM) & [CAL, CNL, DEN, SP, PE, GR, RLLD] & 0.0932 & 0.0244 & 0.96 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: XGBoost feature variable combination preference results. With the combination of random search and grid search and based on the optimal combination of features, the results of the hyper-parameter selection are obtained as Table 4. The results show that the optimal feature combination of the POR model is [AC, CAL, CNL, DEN, SP, RLLS, PE, GR]. Following the hyper-parameter optimization step, the MAE of the training set is 0.0008, and that of the test set is 0.0671; the R\({}^{2}\) of the training set is 0.99, and that of the test set is 0.72. The optimal feature combination of the PERM model is [AC, CAL, SP, RLLS, PE, GR, RLLD], and after the hyper-parameter optimization step, the MAE of the training set is 0.0017, the MAE of the test set is 0.0084, the R\({}^{2}\) of the training set is 0.99, and the R\({}^{2}\) of the test set is 0.32.