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arXiv · 2006.16341

Handling Missing Data in Decision Trees: A Probabilistic Approach

Pasha Khosravi·Antonio Vergari·YooJung Choi·Yitao Liang·Guy Van den Broeck
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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.

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Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang, Guy Van den Broeck. 2020-06-29. Handling Missing Data in Decision Trees: A Probabilistic Approach. https://arxiv.org/abs/2006.16341

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