mlsanityData to 7 Oct 2026

Aleatoric and Epistemic Uncertainty with Random Forests

arXiv 2001.00893 ↗First posted 3 Jan 2020cs.LG stat.MLcited by 42

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Abstract (arXiv)

Due to the steadily increasing relevance of machine learning for practical applications, many of which are coming with safety requirements, the notion of uncertainty has received increasing attention in machine learning research in the last couple of years. In particular, the idea of distinguishing between two important types of uncertainty, often refereed to as aleatoric and epistemic, has recently been studied in the setting of supervised learning. In this paper, we propose to quantify these uncertainties with random forests. More specifically, we show how two general approaches for measuring the learner's aleatoric and epistemic uncertainty in a prediction can be instantiated with decision trees and random forests as learning algorithms in a classification setting. In this regard, we also compare random forests with deep neural networks, which have been used for a similar purpose.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 8; 2023: 9; 2024: 5; 2025: 10; 2026: 4.

42 papers.

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  1. 2021DEUP: Direct Epistemic Uncertainty Predictioncited by 102
  2. 2022Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisationcited by 65
  3. 2023Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?cited by 40
  4. 2023Explaining Predictive Uncertainty with Information Theoretic Shapley Valuescited by 36
  5. 2024Conformalized Credal Set Predictorscited by 21
  6. 2024Explainability through uncertainty: Trustworthy decision-making with neural networkscited by 12
  7. 2023Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applicationscited by 11
  8. 2025Integral Imprecise Probability Metricscited by 11
  9. 2021Dense Uncertainty Estimationcited by 9
  10. 2024Uncertainty quantification for probabilistic machine learning in earth observation using conformal predictioncited by 9
  11. 2022On the Calibration of Probabilistic Classifier Setscited by 8
  12. 2022Uncertainty-Aware Mixed-Variable Machine Learning for Materials Designcited by 6
30 more
  1. 2023Model-agnostic variable importance for predictive uncertainty: an entropy-based approachcited by 6
  2. 2021Active learning for imbalanced data under cold startcited by 5
  3. 2021GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environmentscited by 5
  4. 2025Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisitioncited by 5
  5. 2021Uncertainty-Aware Boosted Ensembling in Multi-Modal Settingscited by 4
  6. 2022Bayesian Deep Learning with Multilevel Trace-class Neural Networkscited by 4
  7. 2025Robustness quantification: a new method for assessing the reliability of the predictions of a classifiercited by 4
  8. 2022Towards Clear Expectations for Uncertainty Estimationcited by 2
  9. 2023Predicting small molecules solubilities on endpoint devices using deep ensemble neural networkscited by 2
  10. 2023Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantificationcited by 2
  11. 2025Robust Explanations Through Uncertainty Decomposition: A Path to Trustworthier AIcited by 2
  12. 2021Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learningcited by 1
  13. 2024Prompting the Unknown: Understanding Response Uncertainty in Large Language Modelscited by 1
  14. 2025Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisionscited by 1
  15. 2025Deep Ensembles for Epistemic Uncertainty: A Frequentist Perspectivecited by 1
  16. 2025Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifiercited by 1
  17. 2026Subjective Risk Decomposition: A New View for Uncertainty Quantificationcited by 1
  18. 2022Combining Predictions under Uncertainty: The Case of Random Decision Treescited by 0
  19. 2022Uncertainty in Extreme Multi-label Classificationcited by 0
  20. 2022Online Learning Models for Vehicle Usage Prediction During COVID-19cited by 0
  21. 2023A Meta-heuristic Approach to Estimate and Explain Classifier Uncertaintycited by 0
  22. 2023U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Stagingcited by 0
  23. 2023Model Uncertainty based Active Learning on Tabular Data using Boosted Treescited by 0
  24. 2024Evaluation of Predictive Reliability to Foster Trust in Artificial Intelligence. A case study in Multiple Sclerosiscited by 0
  25. 2025Conceptualizing Uncertainty: A Concept-based Approach to Explaining Uncertaintycited by 0
  26. 2025Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensemblescited by 0
  27. 2025Uncertainty in Machine Learningcited by 0
  28. 2026Uncertainty Gating for Cost-Aware Explainable Artificial Intelligencecited by 0
  29. 2026Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decompositioncited by 0
  30. 2026Modelling magnetic material properties with uncertainty-aware neural networkscited by 0