Handling Missing Data in Decision Trees: A Probabilistic Approach
Abstract
Decision trees are a popular family of models due to their attractive properties such as interpretability and ability to handle heterogeneous data. Concurrently, missing data is a prevalent occurrence that hinders performance of machine learning models. As such, handling missing data in decision trees is a well studied problem. In this paper, we tackle this problem by taking a probabilistic approach. At deployment time, we use tractable density estimators to compute the "expected prediction" of our models. At learning time, we fine-tune parameters of already learned trees by minimizing their "expected prediction loss" w.r.t.\ our density estimators. We provide brief experiments showcasing effectiveness of our methods compared to few baselines.
Cite
@article{arxiv.2006.16341,
title = {Handling Missing Data in Decision Trees: A Probabilistic Approach},
author = {Pasha Khosravi and Antonio Vergari and YooJung Choi and Yitao Liang and Guy Van den Broeck},
journal= {arXiv preprint arXiv:2006.16341},
year = {2020}
}