Alvaro H. C. Correia, Robert Peharz, Cassio P. de Campos
TL;DR本文将决策树和随机森林重新解释为生成模型,从而引入了一种能够处理缺失数据和异常检测的新型混合生成-判别模型族。通过在实验中与 K 近邻插补等处理缺失数据的方法进行比较,我们发现该模型能够自然地处理数据缺失和异常值检测。
Abstract
decision trees (DTs) and random forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist. Due to their discriminative nat