// ◇ Classical Machine Learning
Decision Trees (CART)
Splits data with a sequence of yes/no questions into pure groups.
决策树（CART） Beginner core
IN PLAIN WORDS
At each step pick the feature/threshold that best separates labels and split, until leaves are pure. Single trees are interpretable but overfit, so they are usually ensembled.
Used in: Rule extraction, risk control, interpretable models
TIMELINE · prerequisites → this → what builds on it
01Original paper
! No single verifiable original paper
02Where it sits
Learn the left column first; the right column builds on this one.
Learn first
- No prerequisites — a good starting point
Decision Trees (CART)
Then you can learn
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: partial, checked 2026-10-21.
- JSON: /api/learn/algorithms.json (id:
decision-tree)