快速可解读的贪树总和
Yan Shuo Tan1, Chandan Singh2,3, Keyan Nasseri2
1Department of Statistics and Data Science, National University of Singapore, Singapore 119077, Republic of Singapore.
概括
快速可解释的贪树总和 (FIGS) 通过总结决策树来提高机器学习的解释性,适应附加结构以获得更好的预测. 这种方法,特别是G-FIGS,在不牺牲准确性或理解性的情况下改进了临床决策工具.
科学领域:
- 机器学习 机器学习
- 医疗信息学 医疗信息学
- 计算统计学 计算统计学
背景情况:
- 现代机器学习模型往往缺乏可解释性,这是医学等高风险领域的关键因素.
- 传统的可解释决策树 (例如,CART) 显示了对附加结构的诱导偏差.
- 需要可解释的模型,能够捕捉数据中的复杂,加法关系.
研究的目的:
- 引入快速可解释的贪树总和 (FIGS),这是一个新的算法,将CART泛化为多个树的总和,以提高可解释性和性能.
- 通过一种称为组概率加权树总和 (G-FIGS) 的变体,适应FIGS用于临床决策工具 (CDI),解决医疗数据异质性.
- 从理论上分析FIGS的解属性及其在学习添加模型中的效率.
主要方法:
- FIGS将分类和回归树 (CART) 泛化,通过在总和中生长多棵树,将逻辑规则与加法结合起来.
- G-FIGS是为了学习CDI而开发的,考虑到医疗数据的异质性和提高特异性.
- 通过使用类似于随机森林的差异减小技术,引入了集体方法FIGS,以减轻过度拟合.
主要成果:
- 在现实数据集上,FIGS实现了最先进的预测性能.
- G-FIGS 衍生出 CDI,其特异性比 CART 提高了多达 20%,同时保持了灵敏度和可解释性.
- 理论分析表明,FIGS实现了脱,使得增量回归函数的更有效的概括成为可能.
- 包装-FIGS显示了与随机森林和XGBoost.对抗的竞争性表现.
结论:
- FIGS为传统的机器学习模型提供了强大而可解释的替代方案,特别是在需要解释性的领域.
- G-FIGS为开发可靠和可解释的临床决策工具提供了一个有价值的工具.
- FIGS的解属性有助于对树和模型及其概括能力的更深入的理论理解.
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