Research breakthrough · curated entry
Breiman proposes random forests
Random Forests
- Date
- October 2001
- Category
- Research breakthrough
- Organization / Model
- University of California, Berkeley
- Evidence
- 1 source · 1 evidence family
The Machine Learning paper bootstraps samples, draws random feature subsets, and lets many decision trees vote. It delivers stable accuracy with little tuning and reports out-of-bag error and feature importance, remaining the default baseline for tabular and industrial modelling.
Curator's note: Cross-checked against the journal DOI.
Curated sources · 1
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This page is a curated static snapshot and can be cited directly. Preferred format GB/T 7714—2025 (electronic resource [EB/OL]); the other 5 styles open below, together with the machine-readable files.
GB/T 7714—2025 (electronic resource [EB/OL])
AI 纪年. Breiman 提出随机森林，集成学习成为经典方法[EB/OL]. (2001-10)[2026-11-03]. https://aihist.net/e/random-forest-2001.html.
APA 7 · American Psychological Association
AI 纪年. (2001, October). Breiman 提出随机森林，集成学习成为经典方法 [Web page]. https://aihist.net/e/random-forest-2001.html
MLA 9 · Modern Language Association
"Breiman 提出随机森林，集成学习成为经典方法." AI 纪年, Oct. 2001, aihist.net/e/random-forest-2001.html. Accessed 3 Nov. 2026.
Chicago 17 · notes-bibliography
AI 纪年. "Breiman 提出随机森林，集成学习成为经典方法." Published Oct. 2001. Accessed Nov. 3, 2026. https://aihist.net/e/random-forest-2001.html.
IEEE · citation-reference list
"Breiman 提出随机森林，集成学习成为经典方法," AI 纪年, Oct. 2001. [Online]. Available: https://aihist.net/e/random-forest-2001.html. [Accessed: 3 Nov. 2026].
ACM · author-date variant
AI 纪年. October 2001. Breiman 提出随机森林，集成学习成为经典方法. Retrieved November 3, 2026 from https://aihist.net/e/random-forest-2001.html.
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