## 5 Rotor Temperature Modeling and Prediction
### Performance Comparison Between RIME-XGBoost and Baseline XGBoost  
As discussed in Sections 5.1 and 5.2, the comprehensive predictive performance of RIME-XGBoost surpasses that of several comparable hybrid models. Section 5.3 evaluates the performance of RIME-XGBoost against baseline XGBoost models using empirical parameters. The goal is to investigate whether the hyperparameters optimized by RIME significantly outperform heuristic settings and to determine if RIME effectively addresses the limitations of XGBoost in hyperparameter selection. Two sets of commonly used empirical parameters were designed for the baseline XGBoost models, as shown in Table 8. The first parameter set is tailored for simpler prediction tasks. It employs fewer trees to avoid overfitting, shallower tree depths to  
Figure 7: Boxplot with dual-Y-axes: R-squared and RMSE of test set across models. prevent excessive model complexity, and a moderate learning rate. The configuration is termed XGBoost-Alpha. The second parameter set is designed for more complex regression tasks. It uses a larger number of trees and deeper tree depths to enhance model precision, coupled with a smaller learning rate to ensure stable convergence. This configuration is referred to as XGBoost-Beta. First, baseline models were evaluated using Group A, with 20 prediction experiments conducted separately. The data partitioning strategy and computational hardware remained consistent across all the experiments. Table 9 presents the average and median values and standard deviations of the error metrics. RIME-XGBoost demonstrates a clear superiority over the baseline models in the experimental evaluations. On the test set, its RMSE is notably lower (only 40% of XGBoost-Alpha's and 16% of XGBoost-Beta's), which highlights its generalization ability. RIME-XGBoost achieves an R\({}^{2}\) score of 0.9972 on the test set, nearly matching the training set's 0.9999. The consistency underscores its resistance to overfitting, which is a challenge partially addressed but not fully resolved by the baselines. They exhibit higher error rates and variability. The results also show the pitfalls of empirical parameter tuning. XGBoost-Alpha, with its limited tree count and shallow depths, underfits the data due to insufficient complexity, as seen in its persistent positive bias (MBE) on the test set. It is a sign of poor accuracy for high-value predictions. Meanwhile, XGBoost-Beta's deeper trees and larger ensemble size lead to instability, resulting in elevated errors. These outcomes reveal that conventional parameters struggle to balance predictive performance and precision for XGBoost. In contrast, RIME's optimized hyperparameters provide a harmonious synergy that increases model robustness and reduces the reliance on trial-and-error tuning. Subsequently, 20 prediction experiments were conducted using the Group B dataset the for baseline models. The average values and standard deviations of all the error metrics are summarized in Table 10.  
In small-sample scenarios, RIME-XGBoost outperforms the two baseline models in terms of predicting rotor temperature. XGBoost-Alpha has a higher test set RMSE than RIME-XGBoost but is better than XGBoost-Beta. The MBE still shows a systematic positive bias. XGBoost-Beta has multiple test set errors significantly higher than other models, suggesting severe overfitting in small samples. Its average R\({}^{2}\) is only 0.7830. The model fails to learn data patterns effectively. However, RIME-XGBoost has potential overfitting risks. The significant difference between the test set and training set R\({}^{2}\) indicates that the model may fail to balance complexity due to insufficient data in small samples. The model structure is too complex. Moreover, its training set RMSE is close to 0, and the MSE is almost 0, implying possible overfitting on the training set.