Gradient boosting
lastmod 2026-08-03created 2026-07-318 edits
Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals rather than the ordinary residuals used in traditional boosting. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees. It usually outperforms random forests. A Korean: 그래디언트 부스팅 트리 model is built in a stage-wise fashion as in other boosting methods, but it generalizes the other methods by allowing optimization of an arbitrary differentiable loss function.
Gradient boosted trees
- An intuitive understanding of the Gradient Boosting Algorithm :: Deep Play
- Gradient Boosted Decision Trees | Machine Learning | Google for Developers
See also
- Machine learning
- Classification
- Regression
- Decision Tree
- AdaBoost
- Korean: 랜덤 포레스트
- XGBoost
- CatBoost
- Decision tree learning
- Boosting (machine learning)