Reference · Glossary
Decision tree
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A **decision tree** splits data with if/else rules to predict a class or value. Easy to visualize; easy to overfit if grown too deep.
#When to use
Tabular problems where interpretability matters: credit rules sketches, triage policies, feature interaction exploration.
#When not to
Raw text/image tasks (use deep models), or when a single deep tree memorizes noise — prefer forests or regularized models.
#Quality checklist
- Limit depth / min samples per leaf
- Compare against a simple baseline
- Inspect feature importances with skepticism
- Validate on held-out time periods if data drifts
#Learn next
- Lesson: `decision-trees`
- Related: random forest