Theory and Practice of Real-World Data Analysis with Ensemble Tree Models and Shapley Additive Explanations

In recent years, artificial intelligence (AI) has become deeply embedded in our daily lives, with machine learning—one of its key components—gaining increasing attention. Although machine learning can achieve high predictive accuracy, it has not been widely adopted in data analysis due to the difficulty in interpreting its results. However, this barrier is beginning to break down with the advent of Explainable AI (XAI) technologies. Among the various types of machine learning algorithms, treebased methods—such as decision trees and ensemble trees—are particularly notable.For tabular data commonly found in medical records, ensemble tree methods often outperform other approaches in both accuracy and scalability. This paper focuses on building machine learning models using ensemble trees and interpreting them with SHAP (Shapley Additive Explanations), a widely used XAI technique. Ensemble tree models can be constructed almost automatically once the data is properly prepared.By using SHAP summary plots and dependence plots, we can gain insights into the overall structure of the data without requiring domain-specific expertise. Although these results may include confounding factors, this approach can still be valuable for uncovering potential medical knowledge.