基于PyTorch实现可解释的软决策树，性能媲美主流模型
Soft Decision Tree classifier: explainable and extendable PyTorch implementation
- 用PyTorch构建软决策树与短时记忆版本，支持可解释性分析
- 在临床数据上AUC表现与XGBoost相当，优于随机森林等传统方法
- 适合需要模型可解释性的医疗分类场景，代码开源可复现
我们使用PyTorch实现了软决策树（SDT）和短时记忆软决策树（SM-SDT），并在模拟数据和临床数据集上进行了广泛测试。通过可视化展示了其可解释性潜力。SDT、SM-SDT与XGBoost在各项数据集上的曲线下面积（AUC）表现相近，均优于随机森林、逻辑回归和传统决策树。临床数据结果表明，除传统决策树外，所有测试分类方法性能相当。相关代码与数据已公开于GitHub：https://github.com/KI-Research-Institute/Soft-Decision-Tree。
原文摘要 · Abstract (English)
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
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