Seonglae ChoGradient Boosting is a Generalization of boosting to arbitrary loss function
Similar to AdaBoost, Gradient Boosting builds upon previous models by adding new models that compensate for the errors of earlier ones. However, it takes a different approach:
- Forward stagewise boosting adds at each steps the tree that reduces the loss the most (given the current model)
- Instead of updating sample weights at each learning stage, it trains new models on the residual errors from previous stages
- This focus on residual errors allows for more precise error correction and model improvement
Gradient Boosting Tools
linearboost-classifier
LinearBoost • Updated 2024 May 22 4:7
ML simple works - A Gentle Introduction to Gradient Boosting
부스팅 알고리즘은 약한 학습기weak learner를 순차적으로 학습시키고 개별 학습기의 예측을 모두 더해 최종 예측을 만들어내는 앙상블 메소드의 한 종류입니다. 그 중 그래디언트 부스팅은 강력한 성능으로 가장 많이 애용되는 알고리즘입니다.
https://metamath1.github.io/blog/posts/gradientboost/gradient_boosting.html
Gradient boosting
Gradient boosting is a machine learning technique used in regression and classification tasks, among others. 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 forest. A gradient-boosted trees 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.
https://en.wikipedia.org/wiki/Gradient_boosting
