## 3 Results and Discussion  
### Model Fitting Results  
The findings of applying different decision tree machine learning algorithms, such as the bagged decision tree, boosted decision tree, and gradient boosted decision tree to forecast the emission of greenhouse gases in Saudi Arabia are provided in this section. In addition, the results obtained from the DT models are compared with the results of the fundamental machine learning model, namely the feedforward neural network, to emphasize their efficacy. Detailed information about the FFNN can be found in [57; 58; 59; 60]. The hyper-parameters of the models are shown in Table 1.  
Figure 6 shows the development of the three decision tree models. The scatter plot of training and test datasets for the bagged decision tree model can be seen in Figure 6a,b. As is shown, 35 samples were used to train the bagged decision tree model while 17 samples were used to test the performance of the developed model. The performance of the bagged decision tree model at predicting the GHG emissions of Saudi Arabia is visualized by plotting the actual GHG emissions and bagged DT estimated GHG emissions (Figure 6c). It is observed that for most of the data points, the actual and predicted data are very close, which guarantees robust predictions by the proposed algorithm. Data points 8 and 12 show fewer prediction errors, and data points 10 and 15 show more prediction errors. According to the scatter plot of the boosted DT model (Figure 6a,b), 17 samples are used to test the model, while 35 samples are used to train the model. The emission prediction performance of the boosted DT is visualized in Figure 6c. It shows that in some data points, the predicted and the actual GHG emissions are very close. For example, data point 8 has the same predicted and actual emissions as the boosted DT model. The highest prediction errors are seen for data points 1, 10, and 14. In addition to the bagged and boosted DT models, the GHG emissions are also predicted with the gradient-boosted DT. The scatter plot of the training and test data points for the gradient-boosted DT model is shown in Figure 6a,b. The performance of the developed gradient-boosted DT model is visualized by plotting the actual emissions and the estimated emissions in CO\({}_{2}\) equivalent tons (Figure 6c). The prediction results from the machine learning algorithms are presented and the forecast accuracy is assessed using different indices such as R\({}^{2}\), RMSE, MAPE, RSR, and WIA. Table 2 shows the statistical performance measures of three decision tree models for both training and testing datasets. The bagged decision tree shows the best R\({}^{2}\) performance with a value of 0.901 for the testing dataset. The same algorithm also shows the lowest RMSE of 0.84 and MAPE of 0.29, indicating the best performance. Even when compared to FFNN, bagged decision tree shows better performance. Similar observations have been made by other researchers who used DT-based AI models. Using data from four Chinese megacities between 2003 and 2017, [PERSON] et al. [61] applied the XGBoost model to identify factors influencing CO\({}_{2}\) emissions. With a root mean square error of only 0.036, the developers of the XGBoost model emphasize its superior applicability and accuracy when estimating the carbon emissions of growing megacities. CO\({}_{2}\) emissions continue to be primarily driven by population, land area, and GDP, albeit under the combined influence of several other factors. Predictions made using XGBoost were also proven to be highly accurate by [PERSON] and [PERSON] [62]. The method was employed by the authors to make projections about CO\({}_{2}\) emissions in Chinese cities. Greenhouse gas generation (CO\({}_{2}\), CH\({}_{4}\)) intensity in a specific region of the cryolithozone was forecasted using XGBoost by [PERSON] et al. [63] utilizing data from a different geographic network of multifunctional monitoring stations, with high accuracy over the medium to long term.