mlsanityData to 14 Oct 2026

A Random Forest Guided Tour

arXiv 1511.05741 ↗First posted 18 Nov 2015math.ST stat.ML stat.THcited by 168

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Abstract (arXiv)

The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method. The approach, which combines several randomized decision trees and aggregates their predictions by averaging, has shown excellent performance in settings where the number of variables is much larger than the number of observations. Moreover, it is versatile enough to be applied to large-scale problems, is easily adapted to various ad-hoc learning tasks, and returns measures of variable importance. The present article reviews the most recent theoretical and methodological developments for random forests. Emphasis is placed on the mathematical forces driving the algorithm, with special attention given to the selection of parameters, the resampling mechanism, and variable importance measures. This review is intended to provide non-experts easy access to the main ideas.

Key facts. Cited by 168 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2015.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 22; 2022: 32; 2023: 30; 2024: 34; 2025: 31; 2026: 19.

168 papers.

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  1. 2024HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?cited by 54
  2. 2022Adversarial random forests for density estimation and generative modelingcited by 44
  3. 2021Classification with abstention but without disparitiescited by 28
  4. 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 25
  5. 2023Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesiscited by 25
  6. 2021Optimal randomized classification treescited by 16
  7. 2021Bridging observation, theory and numerical simulation of the ocean using Machine Learningcited by 15
  8. 2024Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smootherscited by 15
  9. 2021Unveiling the potential of Graph Neural Networks for robust Intrusion Detectioncited by 14
  10. 2021Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forestcited by 13
  11. 2021A cautionary tale on fitting decision trees to data from additive models: generalization lower boundscited by 9
  12. 2022Action Conditioned Tactile Prediction: case study on slip predictioncited by 8
38 more
  1. 2022A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learningcited by 8
  2. 2021Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learningcited by 7
  3. 2022IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Datasetcited by 7
  4. 2024Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfittingcited by 6
  5. 2021On the Trustworthiness of Tree Ensemble Explainability Methodscited by 5
  6. 2022Counter Hate Speech in Social Media: A Surveycited by 5
  7. 2023Optimal Weighted Random Forestscited by 5
  8. 2024An Evaluation of Large Language Models in Bioinformatics Researchcited by 5
  9. 2025Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understandingcited by 5
  10. 2021Multivariate Data Explanation by Jumping Emerging Patterns Visualizationcited by 4
  11. 2021Minimax Rates for High-Dimensional Random Tessellation Forestscited by 4
  12. 2021There is no Double-Descent in Random Forestscited by 4
  13. 2022Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performancecited by 4
  14. 2022Self-supervised learning -- A way to minimize time and effort for precision agriculture?cited by 4
  15. 2023Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRIcited by 4
  16. 2023Stability of Random Forests and Coverage of Random-Forest Prediction Intervalscited by 4
  17. 2024Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysiscited by 4
  18. 2024Open Set Recognition for Random Forestcited by 4
  19. 2024Free Lunch in the Forest: Functionally-Identical Pruning of Boosted Tree Ensemblescited by 4
  20. 2021Feature Analyses and Modelling of Lithium-ion Batteries Manufacturing based on Random Forest Classificationcited by 3
  21. 2021Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinementcited by 3
  22. 2023Machine Learning technique for isotopic determination of radioisotopes using HPGe γ-ray spectracited by 3
  23. 2023RF+clust for Leave-One-Problem-Out Performance Predictioncited by 3
  24. 2023Enhancing Supply Chain Resilience: A Machine Learning Approach for Predicting Product Availability Dates Under Disruptioncited by 3
  25. 2023Landslide Topology Uncovers Failure Movementscited by 3
  26. 2023Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360cited by 3
  27. 2024TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Predictioncited by 3
  28. 2024A Survey of Features Used for Representing Black-box Single-objective Continuous Optimizationcited by 3
  29. 2025Autoencoding Random Forestscited by 3
  30. 2026Australian Bushfire Intelligence with AI-Driven Environmental Analyticscited by 3
  31. 2026Decorrelation, Diversity, and Emergent Intelligence: The Isomorphism Between Social Insect Colonies and Ensemble Machine Learningcited by 3
  32. 2022Manifoldron: Direct Space Partition via Manifold Discoverycited by 2
  33. 2022Don't Explain Noise: Robust Counterfactuals for Randomized Ensemblescited by 2
  34. 2022A Mathematical Programming Approach to Optimal Classification Forestscited by 2
  35. 2022Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecastingcited by 2
  36. 2022AI-enabled exploration of Instagram profiles predicts soft skills and personality traits to empower hiring decisionscited by 2
  37. 2023FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Softwarecited by 2
  38. 2023Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Predictioncited by 2

50 most cited of 168 All 168 (JSON)