M07.5 CONNECT THE MECHANISM
Combine many imperfect predictors
One decision tree is jumpy. Fifty trees voting, or a chain of small trees each fixing the last one's mistakes, give the models that still win most spreadsheet problems.
LESSON OVERVIEW12 min lesson
Lesson overview
One decision tree is jumpy. Fifty trees voting, or a chain of small trees each fixing the last one's mistakes, give the models that still win most spreadsheet problems.
What you’ll explore
- Bagging reduces instability by averaging varied fits; random forests add feature randomness, while boosting builds an additive model that targets remaining error.
GO TO THE SOURCE
Original explanations, connected to the research.
An Introduction to Statistical Learning — authors’ materialsBreiman (2001), Random ForestsChen & Guestrin (2016), XGBoost: A Scalable Tree Boosting SystemSuggest a correction
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