// ◇ Classical Machine Learning
Random Forest
Trains many randomized decision trees and lets them vote.
随机森林 2001 Beginner core
IN PLAIN WORDS
Each tree sees a random sample of rows and features, so trees differ; averaging cancels their errors. It is robust with little tuning and a strong tabular baseline.
Used in: Tabular classification/regression, feature importance
01Original paper
Random Forests
Breiman et al. · Machine Learning · 2001
DOI ↗ ✓ Verified · Crossref API doi:10.1023/A:1010933404324
02Where it sits
Learn the left column first; the right column builds on this one.
Learn first
Then you can learn
- Nothing in the atlas builds on it yet
Related:Gradient Boosting (GBDT)XGBoost
Left: prerequisites · right: builds on this · dashed: related. Full map →
03Learning resources
Official docs, explainers, courses and reference code. All links checked.
04Where the course teaches it
On learning paths:Zero to Deep Learning
For AI / data
- Verification: verified, checked 2026-10-21.
- JSON: /api/learn/algorithms.json (id:
random-forest)