# A Random Forest Guided Tour

arXiv: https://arxiv.org/abs/1511.05741 · HTML: https://mlsanity.com/paper/1511.05741/

## Key facts

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

## Built on by (most cited first, 30 of 168)

- [HARGPT: Are LLMs Zero-Shot Human Activity Recognizers?](https://mlsanity.com/paper/2403.02727/) (2024)
- [Adversarial random forests for density estimation and generative modeling](https://mlsanity.com/paper/2205.09435/) (2022)
- [Classification with abstention but without disparities](https://mlsanity.com/paper/2102.12258/) (2021)
- [A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation](https://mlsanity.com/paper/2304.02858/) (2023)
- [Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis](https://mlsanity.com/paper/2311.10776/) (2023)
- [Optimal randomized classification trees](https://mlsanity.com/paper/2110.11952/) (2021)
- [Bridging observation, theory and numerical simulation of the ocean using Machine Learning](https://mlsanity.com/paper/2104.12506/) (2021)
- [Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers](https://mlsanity.com/paper/2402.01502/) (2024)
- [Unveiling the potential of Graph Neural Networks for robust Intrusion Detection](https://mlsanity.com/paper/2107.14756/) (2021)
- [Trees, Forests, Chickens, and Eggs: When and Why to Prune Trees in a Random Forest](https://mlsanity.com/paper/2103.16700/) (2021)
- [A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds](https://mlsanity.com/paper/2110.09626/) (2021)
- [Action Conditioned Tactile Prediction: case study on slip prediction](https://mlsanity.com/paper/2205.09430/) (2022)
- [A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning](https://mlsanity.com/paper/2211.02166/) (2022)
- [Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning](https://mlsanity.com/paper/2102.12328/) (2021)
- [IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset](https://mlsanity.com/paper/2203.16365/) (2022)
- [Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting](https://mlsanity.com/paper/2401.10359/) (2024)
- [On the Trustworthiness of Tree Ensemble Explainability Methods](https://mlsanity.com/paper/2110.00086/) (2021)
- [Counter Hate Speech in Social Media: A Survey](https://mlsanity.com/paper/2203.03584/) (2022)
- [Optimal Weighted Random Forests](https://mlsanity.com/paper/2305.10042/) (2023)
- [An Evaluation of Large Language Models in Bioinformatics Research](https://mlsanity.com/paper/2402.13714/) (2024)
- [Exploring the Capabilities of LLMs for IMU-based Fine-grained Human Activity Understanding](https://mlsanity.com/paper/2504.02878/) (2025)
- [Multivariate Data Explanation by Jumping Emerging Patterns Visualization](https://mlsanity.com/paper/2106.11112/) (2021)
- [Minimax Rates for High-Dimensional Random Tessellation Forests](https://mlsanity.com/paper/2109.10541/) (2021)
- [There is no Double-Descent in Random Forests](https://mlsanity.com/paper/2111.04409/) (2021)
- [Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance](https://mlsanity.com/paper/2201.12692/) (2022)
- [Self-supervised learning -- A way to minimize time and effort for precision agriculture?](https://mlsanity.com/paper/2204.02100/) (2022)
- [Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI](https://mlsanity.com/paper/2306.14080/) (2023)
- [Stability of Random Forests and Coverage of Random-Forest Prediction Intervals](https://mlsanity.com/paper/2310.18814/) (2023)
- [Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis](https://mlsanity.com/paper/2401.03530/) (2024)
- [Open Set Recognition for Random Forest](https://mlsanity.com/paper/2408.02684/) (2024)

## 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.