# Handling Missing Data in Decision Trees: A Probabilistic Approach

arXiv: https://arxiv.org/abs/2006.16341 · HTML: https://mlsanity.com/paper/2006.16341/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 9 of 9)

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- [Probabilistic Sufficient Explanations](https://mlsanity.com/paper/2105.10118/) (2021)
- [A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries](https://mlsanity.com/paper/2102.06137/) (2021)
- [Verifiable Goal Recognition for Autonomous Driving with Occlusions](https://mlsanity.com/paper/2206.14163/) (2022)
- [Towards Narrowing the Generalization Gap in Deep Boolean Networks](https://mlsanity.com/paper/2409.05905/) (2024)
- [When is the Computation of a Feature Attribution Method Tractable?](https://mlsanity.com/paper/2501.02356/) (2025)
- [Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI](https://mlsanity.com/paper/2404.04686/) (2024)
- [A Sobering Look at Tabular Data Generation via Probabilistic Circuits](https://mlsanity.com/paper/2603.23016/) (2026)
- [On Logic-Based Explainability with Partially Specified Inputs](https://mlsanity.com/paper/2306.15803/) (2023)

## Abstract (arXiv)

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.