## 4 Experimental Analysis
### Presentation of the Result of Training Models
The sharp rise and flattening of the curve suggest that XGBoost's boosting mechanism effectively captures relevant features and relationships, providing strong predictive performance. Figure 3: The Bootstrap Aggregating (Bagging) model ROC curve. Figure 2: The ROC curve of decision tree. Similarly, the ROC curve for the random forest model achieves a good balance with the smoothness of the curve (see Figure 5). This demonstrates that random forest is consistent and handles variance well, capturing relevant patterns without overfitting. The Extra Trees model ROC curve demonstrated that the model is proficient at distinguishing between positive and negative classes (See Figure 6). Extra Trees uses a high  
Figure 4: The XGBoost’s model ROC curve. Figure 5: The random forest model ROC curve. degree of randomness in both feature selection and split points, which helps reduce overfitting and improve generalization. The ROC curve for an XGBoost model indicates that the model is highly effective at distinguishing between the positive and negative classes. XGBoost uses boosting techniques that iteratively improve errors from previous rounds, making it highly effective in capturing complex patterns in data. The smooth and high-reaching curve signifies that XGBoost is stable and provides consistent performance across different threshold values (see Figure 7). Figure 6: The Extra Trees model ROC curve. Figure 7: The Extra Trees model ROC curve. The analysis of the GTD dataset within the models has indicated numerous desirable outcomes. This phase represents the second stage of the experimental training employing the GTD dataset. The training results indicate that the performance of all models is adequate. When applied to the GTD dataset before tuning, the models followed a similar pattern. Extra Trees again achieved the highest accuracy (91.04%) but with modest recall (69.23%), leading to a corrected F1-score of 0.7725. XGBoost outperformed all other models with a recalculated F1-score of 0.8247, based on its strong recall (76.61%) and precision (89.31%). This balance is critical in minimizing false negatives, making XGBoost particularly valuable in applications requiring high sensitivity. Random forest and Bagging provided solid mid-range performance, reinforcing their consistency, but again fell short of XGBoost in both recall and the F1-score. Table 8 show the result of The model training performance of GTD. A similar fine-tuning strategy employed in the initial study was also applied here. Model performance metrics improved significantly after fine-tuning and cross-validation. Table 9 presents the hyperparameter tuning details for models trained on the Global Terrorism Database (GTD). Similar to the open data analysis, key parameters such as tree depth, the number of estimators, learning rate, and sampling strategies were tuned to optimize model performance. The GTD dataset, being more structured and feature-rich, benefited from slightly different optimal settings; for instance, the Decision Tree performed better using the gini criterion, and ensemble methods like XGBoost and random forest achieved notable improvements with deeper trees and increased estimators. These fine-tuned values led to higher accuracy and recall, essential for identifying complex patterns in terrorism data. Overall, the tuning ensured that each model was better adapted to the GTD's characteristics, improving predictive reliability compared to default configurations. Post-tuning, XGBoost maintained its dominance, achieving the highest F1-score of 0.8786, with precision (93.89%) and recall (82.52%) both at outstanding levels (see Table 10). Extra Trees reached the highest accuracy (94.22%) but lagged in recall (74.11%) and thus produced a slightly lower F1-score (0.8259). Random forest also showed strong balance (F1-score: 0.8255), but not to the same extent as XGBoost. These results reinforce the earlier insight that accuracy alone is not sufficient to assess model utility.