# Random Forest Missing Data Algorithms

arXiv: https://arxiv.org/abs/1701.05305 · HTML: https://mlsanity.com/paper/1701.05305/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2017.

## Built on by (most cited first, 23 of 23)

- [Adversarial random forests for density estimation and generative modeling](https://mlsanity.com/paper/2205.09435/) (2022)
- [Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values](https://mlsanity.com/paper/2109.10431/) (2021)
- [On the Relation between Prediction and Imputation Accuracy under Missing Covariates](https://mlsanity.com/paper/2112.05248/) (2021)
- [What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?](https://mlsanity.com/paper/2206.10323/) (2022)
- [Stage-Aware Learning for Dynamic Treatments](https://mlsanity.com/paper/2310.19300/) (2023)
- [missForestPredict -- Missing data imputation for prediction settings](https://mlsanity.com/paper/2407.03379/) (2024)
- [Dynamic Topic Language Model on Heterogeneous Children's Mental Health Clinical Notes](https://mlsanity.com/paper/2312.14180/) (2023)
- [Adapting tree-based multiple imputation methods for multi-level data? A simulation study](https://mlsanity.com/paper/2401.14161/) (2024)
- [A Cost-Sensitive Transformer Model for Prognostics Under Highly Imbalanced Industrial Data](https://mlsanity.com/paper/2402.08611/) (2024)
- [kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions](https://mlsanity.com/paper/2509.08366/) (2025)
- [Variable selection with missing data in both covariates and outcomes: Imputation and machine learning](https://mlsanity.com/paper/2104.02769/) (2021)
- [Meta-analysis of individualized treatment rules via sign-coherency](https://mlsanity.com/paper/2211.15476/) (2022)
- [Building RadiologyNET: Unsupervised annotation of a large-scale multimodal medical database](https://mlsanity.com/paper/2308.08517/) (2023)
- [AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers -- A narrative review of a growing field](https://mlsanity.com/paper/2406.17822/) (2024)
- [A Central Limit Theorem for the permutation importance measure](https://mlsanity.com/paper/2412.13020/) (2024)
- [coverforest: Conformal Predictions with Random Forest in Python](https://mlsanity.com/paper/2501.14570/) (2025)
- [Global Ease of Living Index: a machine learning framework for longitudinal analysis of major economies](https://mlsanity.com/paper/2502.06866/) (2025)
- [Challenges and proposed solutions in modeling multimodal medical data: A systematic review](https://mlsanity.com/paper/2505.06945/) (2025)
- [Feature Impact Analysis on Top Long-Jump Performances with Quantile Random Forest and Explainable AI Techniques](https://mlsanity.com/paper/2508.09810/) (2025)
- [Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learning](https://mlsanity.com/paper/2509.03316/) (2025)
- [Localized Uncertainty Quantification in Random Forests via Proximities](https://mlsanity.com/paper/2509.22928/) (2025)
- [Transfer Learning and Machine Learning for Training Five Year Survival Prognostic Models in Early Breast Cancer](https://mlsanity.com/paper/2509.23268/) (2025)
- [Hybrid Methods for Robust Tabular Data Imputation](https://mlsanity.com/paper/2609.39613/) (2026)

## Abstract (arXiv)

Random forest (RF) missing data algorithms are an attractive approach for dealing with missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and they have the potential to scale to big data settings. Currently there are many different RF imputation algorithms but relatively little guidance about their efficacy, which motivated us to study their performance. Using a large, diverse collection of data sets, performance of various RF algorithms was assessed under different missing data mechanisms. Algorithms included proximity imputation, on the fly imputation, and imputation utilizing multivariate unsupervised and supervised splitting---the latter class representing a generalization of a new promising imputation algorithm called missForest. Performance of algorithms was assessed by ability to impute data accurately. Our findings reveal RF imputation to be generally robust with performance improving with increasing correlation. Performance was good under moderate to high missingness, and even (in certain cases) when data was missing not at random.