Decision Tree Ensemble Based Classification of Terrorist Attacks Using eXplainable Artificial Intelligence

The study proposes using five benchmark machine-learning models alongside XGBoost, applied for the first time to an existing case study to predict the success of suicide of terrorist attacks. Utilizing data from the Global Terrorism Database (GTD), the study evaluates model effectiveness to aid decision-making for emergency responders and policymakers. Employing explainable Artificial Intelligence (XAI) models like SHAP ensures transparent decision-making processes. XGBoost performed best for accuracy and performance, while LightGBM excelled in explainability, with SHAP providing global and local insights into their decision-making. The primary goal is to enhance user comprehension and facilitate informed decision-making in critical scenarios, prioritizing transparency, and trustworthiness.