## 4 Discussion
The weak relationships in the correlation matrix between the CH\({}_{4}\) outputs and inputs shown in Figure 7 indicate that the linear dependencies in the data are low. However, machine learning models have successfully learned complex and nonlinear relationships, demonstrating excellent prediction performance. The XGBoost model, with parameter optimization, achieved the highest prediction performance, reaching a correlation coefficient of 0.933 (Figure 11). As seen in Figure 11, even under suboptimal conditions, it outperformed other models with a correlation of 0.856 and an RMSE value of 1.503. These results indicate that the XGBoost model can make CH\({}_{4}\) level predictions successfully and consistently. On the other hand, the prediction performances of the Elastic Net, KNN and DT models significantly decreased under suboptimal conditions, demonstrating that these models have limited capacity to learn nonlinear relationships. The findings of this study demonstrate that machine learning models, particularly the prediction system developed using XGBoost, can predict CO, CO\({}_{2}\), H\({}_{2}\), and CH\({}_{4}\) gas levels in biomass gasification processes with high accuracy and consistency. Bayesian optimization has enhanced accuracy and enabled the acquisition of resilient predictions by optimizing the model parameters. The XGBoost model has outperformed other models in accuracy and robustness. Notably, the superior performance of the XGBoost model under suboptimal conditions cannot be attributed to model regularization or parameter optimization, as both optimal and suboptimal conditions were tested using the same set of parameters. The model's ability to maintain high performance, even under suboptimal conditions, can be attributed to XGBoost's inherent robustness, capacity to handle noisy or incomplete data effectively, and ability to capture complex, suboptimal relationships. Figure 10: Taylor diagram of H\({}_{2}\) level prediction performance under optimal and suboptimal conditions. However, their low robustness has marked the Elastic Net, KNN, and DT models. These models exhibited significant performance differences under optimal and suboptimal conditions, indicating their limited generalization capacity and the substantial impact of parameter optimization on performance. Elastic Net failed to deliver the expected accuracy in biomass gasification processes, where complex and nonlinear relationships dominate. The KNN model, due to its requirement to use the entire dataset for each prediction, resulted in increased computation time for large datasets, leading to a loss of accuracy. Decision Trees, on the other hand, performed poorly on new data outside the training set, due to over-branching and overfitting issues. These challenges limit the applicability of these models in complex and nonlinear processes. The advantages of this study include the development of ML models with different architectures for predicting biomass gas levels and the public sharing of the Python source code. This enables researchers to replicate similar studies and enhance existing models. Furthermore, the prediction performances of the models were tested not only under optimal conditions but also in suboptimal conditions. In this way, the models' abilities to make successful predictions and their robustness were assessed. This approach aids in selecting models capable of making accurate predictions even in worst-case scenarios with real-world data. Finally, it was observed that there are few studies focused on hyperparameter optimization in the literature. This study tested models with different parameters and broad parameter ranges, enhancing their flexibility. As a result, models that are not only optimized with specific parameters but also adaptable to varying conditions, and thus more robust and reliable, were obtained. Despite the clear advantages of the study, some limitations exist. Studies on biomass gasification processes, like the one presented here, often face challenges due to the small size of datasets.