## 3 Research Methodology
### Machine Learning Algorithms for Predictive Modeling
#### 3.1.5 Extra Trees (ETs)  
The training of geometric dependent variables concatenated with lexical threshold confidence measures and geometric dependent variables concatenated with lexical threshold confidence measures and lexical profile features based on random sampling with replacement is also tested using the Extra Trees model [43]. It is observed that Extra Trees can handle high-dimensional data very well. They work well even in small datasets, and they are very robust over filtering or pre-processing in general, especially in special populations that have specific information. Their performance is like that of random forests, but they are different in the way that they get the random subsets of the features only, where they use this subset of features for splitting [44]. In Extra Trees, we take all the samples, and it randomly selects a feature split for the decision tree nodes. It may lead to overfitting, but it could also lead to the model being more robust as we would not have access to the underlying data generating mechanism or the important features related to our generated dataset.  
The Extremely Randomized Trees (Extra Trees) algorithm is an ensemble learning method that extends the principles of random forests by injecting additional randomness into the decision-making process. Like random forests, Extra Trees aggregates the outputs of multiple unpruned decision trees built on different random subsets of the data. However, unlike random forests, where optimal split points are determined based on a criterion like Gini impurity or entropy, Extra Trees selects split thresholds at random for each candidate feature. This added stochasticity reduces variance even further while maintaining low bias, making the model more robust to overfitting--particularly beneficial in high-dimensional, sparse, and noisy data such as text.  
In the context of terrorism prediction, where textual inputs from open sources often contain semantically overlapping terms and an inconsistent structure, Extra Trees is especially advantageous. First, it naturally supports feature selection through its randomized splitting mechanism, which is beneficial when dealing with large TF-IDF matrices. Second, its computational efficiency and parallelizability make it suitable for processing sizeable corpora with high throughput. Third, Extra Trees is resilient to outliers and performs well even when class labels are imbalanced, which is common in datasets related to terrorism, where positive cases (relevant terms or attacks) are much fewer than negative ones (irrelevant or neutral terms).  
In our study, Extra Trees demonstrated strong predictive performance, particularly in terms of precision and accuracy, indicating its ability to classify non-relevant terms conservatively while maintaining a reliable identification of key terrorism-related lexical patterns.  
While this study employs the standard Extra Trees algorithm, we adapted it through domain-aware preprocessing and task-specific hyperparameter tuning to optimize performance in terrorism-related term prediction. The tuning process accounted for the unique lexical characteristics of terrorism data, including high-dimensional TF-IDF vectors, imbalanced class distributions, and contextual ambiguity. Although no structural modification was applied to the base algorithm, the model was integrated into a preprocessing pipeline that effectively enhanced its ability to distinguish between relevant and irrelevant lexical signals. We acknowledge that future work could explore algorithmic improvements tailored specifically for terrorism detection. These may include hybrid approaches combining Extra Trees with deep semantic models (e.g., word embeddings or transformers), or incorporating cost-sensitive learning to further mitigate the effects of false negatives. Such integrations may further improve the model's suitability for operational use in cyber-intelligence environments.