## 4 Discussion
This approach ensured that the selected models performed well and provided consistent results under suboptimal conditions, making them more reliable in real-world applications. The prediction performance of the models was also visualized and analyzed using a Taylor diagram. In the Taylor diagram, the Correlation Coefficient Contours (Arcs) represent the relationships between the observed values and the model predictions. A correlation coefficient approaching 1 indicates high prediction accuracy, while the radius (standard deviation) describes the variations in the models' predictions. Standard deviation values closer to those of the observed data signal more successful models. In the prediction of CO levels, the performances of the models under optimal and suboptimal conditions, based on hyperparameter combinations, are presented comparatively in the Taylor diagram in Figure 8. Upon examining the Taylor diagram, it can be observed that the XGBoost model has a correlation coefficient of 0.951 and a standard deviation (8.51) that aligns closely with the observed data. When comparing the suboptimal prediction performances of the models, it was found that the XGBoost model exhibits the highest correlation coefficient (0.889) and the lowest RMSE value (5.291). In contrast, the KNN model significantly differs between its optimal (R = 0.910) and suboptimal (R = 0.289) performance. This is due to the KNN model's performance varying significantly depending on the sensitivity of its hyperparameter settings and the number of neighbors. The KNN model can yield different results depending on the data density and convergence rate, which causes fluctuations in its performance. Additionally, if the number of neighbors is not correctly determined in high-dimensional data, KNN can lead to issues such as overfitting or underfitting. Therefore, the difference between the optimal and suboptimal prediction performances of the KNN model reflects the model's sensitivity to hyperparameter selection. This suggests that the model's robustness is limited due to performance fluctuations with different parameters. On the other hand, the XGBoost model demonstrates strong performance even in the suboptimal scenario, indicating its robustness. This highlights the consistently high accuracy the XGBoost model provides, even when faced with challenges. The model's flexibility allows for reliable results, even in real-world applications with more prevalent uncertainty and data variability. When examining the optimal CO\({}_{2}\) level prediction results of the models (Figure 9), the XGBoost model stands out as the most successful, with a correlation coefficient of 0.954 and a standard deviation value of 11.06. The fact that the XGBoost model still achieves a correlation coefficient of 0.858 and an RMSE value of 6.463 in the suboptimal condition further indicates its superior performance. On the other hand, the KNN model, with a maximum correlation of 0.934 and a minimum of 0.175 in CO\({}_{2}\) level prediction, demonstrates unreliable and inconsistent performance under poor conditions. Figure 8: Taylor diagram of CO level prediction performance for optimal and suboptimal conditions. Figure 9: Taylor diagram of CO\({}_{2}\) level prediction performance for optimal and suboptimal conditions. When comparing the prediction performances of the models for H\({}_{2}\), it is observed that under optimal conditions, the XGBoost model achieves the highest R value of 0.981 (Figure 10). Similarly, under suboptimal conditions, the XGBoost model outperforms the other models, reaching an R value of 0.941. These findings demonstrate that the XGBoost model offers more robust performance, achieving higher accuracy and more consistent prediction variations than other models, even under suboptimal conditions. The KNN model reached an optimal R value of 0.979, and the 0.316 decrease in performance under suboptimal conditions indicates the model's sensitivity to parameter optimization.