With the ever-increasing use of complex machine learning models in critical
applications within the finance domain, explaining the decisions of the model
has become a necessity. With applications spanning from cr
通过运用博弈论、局部特征交互作用效应和全局模型结构,我们实现了对基于树的机器学习模型的高可解释性,应用于三个医疗机器学习问题,在透露模型全局结构的同时保持其基本特征,识别出美国人口中高强度但低频率的非线性死亡风险因素,突显具有共同危险特征的明显人口亚组,识别出慢性肾脏疾病危险因素之间的非线性交互作用效应,并监测在医院部署的机器学习模型(Identifying factors leading to model's performance decay over time)