Soft Decision Tree classifier: explainable and extendable PyTorch implementation

2025/12/03 by Reuben R. Shamir, Shamir, Reuben R
Computer Science · Health Professions · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Computer Vision and Pattern Recognition (cs.CV) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare

paper · pdf · doi:10.48550/arxiv.2512.11833

openalex publication_date 2025/12/03 · openalex created_date 2025/12/17 · openalex updated_date 2026/07/28

Abstract

We implemented a Soft Decision Tree (SDT) and a Short-term Memory Soft Decision Tree (SM-SDT) using PyTorch. The methods were extensively tested on simulated and clinical datasets. The SDT was visualized to demonstrate the potential for its explainability. SDT, SM-SDT, and XGBoost demonstrated similar area under the curve (AUC) values. These methods were better than Random Forest, Logistic Regression, and Decision Tree. The results on clinical datasets suggest that, aside from a decision tree, all tested classification methods yield comparable results. The code and datasets are available online on GitHub: https://github.com/KI-Research-Institute/Soft-Decision-Tree

Citations

• Distilling a Neural Network Into a Soft Decision Tree

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