标题： 基于距离协方差的随机森林迁移学习：误差界限和电子健康记录应用 显示英文标题

标题： Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Application

摘要： 随机森林由于在结构化表格数据上相对于其他方法的广泛优越性，是机器学习应用的重要方法。 我们提出了一种非参数回归中的迁移学习方法，使用基于距离协方差的特征权重的中心随机森林（CRF），假设未知的源和目标回归函数在少数特征上不同（稀疏不同）。 我们的方法首先通过源域训练的CRF预测目标域的响应，获得残差。 然后，我们在独立样本中，根据特征与残差之间的样本距离协方差，以特征分割概率成比例的方式，对残差拟合另一个CRF。 我们推导了该过程的均方误差率的上界，作为样本大小和差异维度的函数，从理论上证明了随机森林中迁移学习的优势。 在模拟中，我们展示了对于CRF得到的结果也适用于具有数据驱动特征分割选择的标准随机森林（SRF）方法。 除了迁移学习外，我们的结果还显示了在某些情况下基于距离协方差的权重对RF性能的好处。 我们的方法在使用大型多医院电子健康记录数据集（包含20万名ICU患者的数据）预测较小床位目标医院的ICU患者死亡率时表现出显著的提升。 显示英文摘要

摘要： Random forest is an important method for ML applications due to its broad outperformance over competing methods for structured tabular data. We propose a method for transfer learning in nonparametric regression using a centered random forest (CRF) with distance covariance-based feature weights, assuming the unknown source and target regression functions are different for a few features (sparsely different). Our method first obtains residuals from predicting the response in the target domain using a source domain-trained CRF. Then, we fit another CRF to the residuals, but with feature splitting probabilities proportional to the sample distance covariance between the features and the residuals in an independent sample. We derive an upper bound on the mean square error rate of the procedure as a function of sample sizes and difference dimension, theoretically demonstrating transfer learning benefits in random forests. In simulations, we show that the results obtained for the CRFs also hold numerically for the standard random forest (SRF) method with data-driven feature split selection. Beyond transfer learning, our results also show the benefit of distance-covariance-based weights on the performance of RF in some situations. Our method shows significant gains in predicting the mortality of ICU patients in smaller-bed target hospitals using a large multi-hospital dataset of electronic health records for 200,000 ICU patients.