Random Forest

Also known as RF

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Overview

A random forest trains many decision trees on resampled data and random feature subsets, then combines their predictions to improve accuracy and reduce variance. Researchers develop variants for tabular classification and regression, high-dimensional data, longitudinal outcomes, causal effect estimation, online learning, and privacy-preserving prediction. Common approaches adjust tree splits, resampling, or vote weights, while other work studies feature importance, explanations, computational efficiency, and how many trees are needed. Forests remain useful flexible baselines, but choices that help in one setting can hurt another: mean-based splits often suit scalar outcomes, while distributional criteria may help for multivariate responses, and feature importance can be biased by correlations or mixed feature types.

Latest papers

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34 papers5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

  1. Random Jump Forests

    Oct 23, 2026Martin Bladt, Rasmus Frigaard LemvigRandom ForestSurvival Analysis

    Department of Mathematical Sciences, University of Copenhagen

  2. Metric Conformal Prediction via the Conditional Expected Local Radius

    Oct 22, 2026Rui Qiu, Fang Yao, Zhou YuConformal PredictionConditional Distribution Estimation

    School of Statistics, KLATASDS-MOE, East China Normal University · School of Mathematical Sciences, Center for Statistical Science, Peking University

  3. Truncated Out-of-Bag Probability Gain: A Noise-Robust Criterion for Random-Forest Wrapper Feature Selection

    Oct 21, 2026Sejong OhFeature SelectionRandom Forest

    Department of Software Science, Dankook University, Youngin, Republic of Korea

  4. Interval-valued SHAP in Tree-Based Models

    Oct 8, 2026Chenrui Zhu, Vu-Linh Nguyen, Marie-Hélène Masson +1Shapley Value AttributionFeature Attribution

    Université de technologie de Compiègne, UMR-CNRS 7253 Heudiasyc Compiègne, France · IUT de l’Oise, Université de Picardie Jules Verne Beauvais, France

  5. JevForest: Path Voting for Budgeted Feature Acquisition

    Oct 7, 2026Yu YanActive Feature AcquisitionRandom Forest

    University of Chinese Academy of Sciences

  6. Feature Space Selection and Heterogeneous Effect Estimation for Blood-Brain Barrier Permeability: A Random Forest to the Generalized Random Forest Pipeline

    Sep 24, 2026Tshemollo Rapolai, Seite Makgai, Mohammad ArashiConditional Average Treatment Effect EstimationMolecular Property Prediction

    Department of Statistics, University of Pretoria, Pretoria, South Africa · Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P.O. Box 1159, Mashhad, 91775, Iran

  7. Improving the Predictive Performance of Bootstrap Aggregating by Dirichlet Resampling

    Sep 18, 2026Quoc Viet Le, Joonha ParkRandom Forest

    Department of Statistics, University of Wisconsin–Madison · Department of Mathematics, University of Kansas

  8. Splitting the Difference: Interpretable Causal Forests for Treatment Effect Heterogeneity and Bias

    Sep 15, 2026Nicolas Alexander Ihlo, Merle BehrCausal Effect EstimationConditional Average Treatment Effect Estimation

    Faculty of Informatics and Data Science University of Regensburg, Germany

  9. Just add noise: Debiasing tree-based variable importance in mixed data

    Sep 12, 2026Jiahe Li, Omar MelikechiRandom ForestFeature Selection

    Department of Statistical Science, Duke University

  10. Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study

    Aug 7, 2026Yushu Zou, Ye Li, Johra Moosa +3Epidemic ForecastingGradient-Boosted Decision Trees

    Public Health Ontario, Toronto, Canada · Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Canada · Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada +1

  11. Mind the Missing Split: Resolving Feature Heterogeneity in Swarm Learning with Random Forests

    Jul 28, 2026Mohammad Tajabadi, Dominik HeiderLearning with Missing DataDecentralized Learning

    Institute of Medical Informatics, University of Münster, Münster, Germany

  12. Distributional Split Criteria for Random Forests: Extensions, Shrinkage, and the Robustness of Mean Splitting

    Jul 26, 2026Silas KoemenMaximum Mean DiscrepancyRandom Forest

  13. Longitudinal Random Forests for Sparse and Irregular Response Trajectories

    Jul 23, 2026Yangsheng Wang, Xiaotian Dai, Haoda Fu +1Irregular Time-Series ModelingTrajectory Prediction

    Department of Mathematics and Statistics, Binghamton University, Binghamton, New York 13902, USA · Department of Mathematics, Illinois State University, Normal, Illinois 61790, USA · Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA

  14. Conditional Inference Trees and Forests for Feature Selection

    Jul 1, 2026Robert Milletich, Justin Downes, Steve Goley +1Decision Tree LearningFeature Selection

    Amazon Web Services

  15. A Stationary-Distribution Theory for Triplet-Based Plateau Search in Random Forest Ensemble-Size Selection

    Jun 29, 2026Andrey A. Dukhovny, Andrey M. LangeRandom ForestHyperparameter Optimization

    Sberbank Moscow 117997, Russia · Skolkovo Institute of Science and Technology (Skoltech), Moscow 121205, Russia · Federal Research Center “Computer Science and Control” of Russian Academy of Sciences (FRC CSC RAS), Moscow 119333, Russia

  16. A Kernel Fisher Discriminant Analysis-Based Tree Ensemble Classifier: KFDA Forest

    Jun 27, 2026Donghwan Kim, Seung Hwan Park, Jun-Geol BaekEnsemble LearningClassification

    School of Industrial Management Engineering, Korea University Seoul, Korea · School of Mechanical Engineering, Chungnam National University Daejeon, Korea

  17. Correcting Variable Importance Scored by Random Forests

    Jun 9, 2026Guancheng Zhou, Haiping Xu, Jason Liu +1Feature AttributionRandom Forest

    Mathematics and Data Science University of Massachusetts, Dartmouth, MA · Computer and Information Science University of Massachusetts, Dartmouth, MA · The Rivers School, Weston, MA

  18. How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration

    Jun 2, 2026Vadim Porvatov, Andrey Dukhovny, Andrey LangeEnsemble LearningRandom Forest

    Sberbank, Moscow 117997, Russia · Skolkovo Institute of Science and Technology (Skoltech), Moscow 121205, Russia · Federal Research Center ‘‘Computer Science and Control" of Russian Academy of Sciences (FRC CSC RAS), Moscow 119333, Russia

  19. Correcting Split Selection in Online Decision Trees via Anytime-Valid Inference

    May 29, 2026Salim I. Amoukou, Saumitra Mishra, Manuela VelosoAnytime-Valid InferenceDecision Tree Learning

    J.P. Morgan AI Research.

  20. CART Random Forests as Sequential Allocation over Random Opportunity Sets: A Stochastic-Control Theory of Ensemble Risk

    May 26, 2026Tianxing Mei, Yingying Fan, Mingming Leng +1Ensemble LearningStatistical Learning Theory

    Faculty of Business, Lingnan University · Data Sciences and Operations Department, University of Southern California

  21. Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees

    May 21, 2026Christian Janos Lebeda, David Erb, Tudor Cebere +1Privacy-Preserving MLDifferential Privacy

    PreMeDICaL, Inria, Université de Montpellier, INSERM, France · Technical University of Munich, Germany · Work done while visiting PreMeDICaL, Inria.

  22. Decision-Path Patterns as Tree Reliability Signals: Path-based Adaptive Weighting for Random Forest Classification

    May 20, 2026Youngjoon ParkEnsemble LearningBinary Classification

    Independent Researcher

  23. Cross-Paradigm Knowledge Distillation: A Comprehensive Study of Bidirectional Transfer Between Random Forests and Deep Neural Networks for Big Data Applications

    May 19, 2026Mahdi Naser MoghadasiMulti-Teacher Knowledge DistillationKnowledge Distillation

    BrightMind AI Research Seattle, Washington, USA

  24. Minimax Rates and Spectral Distillation for Tree Ensembles

    May 12, 2026Binh Duc Vu, David S. WatsonModel CompressionGradient-Boosted Decision Trees

    King’s College London

  25. Adaptive MSD-Splitting: Enhancing C4.5 and Random Forests for Skewed Continuous Attributes

    Apr 21, 2026Jake LeeDecision Tree LearningRandom Forest

    Independent Researcher · Austin, TX, USA

  26. Rigorous Explanations for Tree Ensembles

    Mar 31, 2026Yacine Izza, Alexey Ignatiev, Xuanxiang Huang +2Explainable Artificial IntelligenceRandom Forest

    National University of Singapore, Singapore · Monash University, Australia · Nanyang Technological University, Singapore +1

  27. Quantum Random Forest for the Regression Problem

    Mar 24, 2026Kamil Khadiev, Liliya SafinaQuantum Machine LearningRandom Forest

    Institute of Computational Mathematics and IT, Kazan Federal University, Kazan, Russia · Zavoisky Physical-Technical Institute, FRC Kazan Scientific Center of RAS, Kazan, Russia

  28. Multi-RF Fusion with Multi-GNN Blending for Molecular Property Prediction

    Mar 21, 2026Zacharie BugaudMolecular Property PredictionEnsemble Learning

  29. Decorrelation, Diversity, and Emergent Intelligence: The Isomorphism Between Social Insect Colonies and Ensemble Machine Learning

    Mar 20, 2026Ernest Fokoué, Gregory Babbitt, Yuval LeventalShared Decision-MakingEnsemble Learning

    School of Mathematics and Statistics, Rochester Institute of Technology · Gosnell School of Life Sciences, Rochester Institute of Technology · Center for Imaging Science, Rochester Institute of Technology

  30. Jump Like A Squirrel: Optimized Execution Step Order for Anytime Random Forest Inference

    Mar 2, 2026Daniel Biebert, Christian Hakert, Kay Heider +3Random ForestEfficient Inference

    TU Dortmund University Dortmund, Germany