Handling Missing Data in Decision Trees: A Probabilistic Approach

• URL: http://arxiv.org/abs/2006.16341v1
• Date: Mon, 29 Jun 2020 19:54:54 GMT
• Title: Handling Missing Data in Decision Trees: A Probabilistic Approach
• Authors: Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang, Guy Van den Broeck
• Abstract summary: We tackle the problem of handling missing data in decision trees by taking a probabilistic approach. 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"
• Score: 41.259097100704324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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