Gradient Boosting
تعزيز التدرج
أسلوب تعلُّم مجمَّع يبني نموذجاً قوياً بضم متعلمين ضعفاء (عادةً أشجار قرار ضحلة) بالتتابع، بحيث يُدرَّب كل متعلم جديد لتصحيح تدرّج خطأ المجموع السابق، وهو الأساس لخوارزميات XGBoost وLightGBM وCatBoost.
An ensemble learning technique that builds a strong model by sequentially combining weak learners — typically shallow decision trees — each trained to fit the gradient of the previous ensemble's loss, underlying algorithms such as XGBoost, LightGBM, and CatBoost.
Also translated asGradient Boosting، التعزيز التدرجي، آلات تعزيز التدرج، GBM
First appears in this corpus in: Classification and Regression Trees (1984)
Appears in these papers
- A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting1997in the sky ✦
- Bagging Predictors1996in the sky ✦
- Classification and Regression Trees1984in the sky ✦
- Greedy Function Approximation: A Gradient Boosting Machine2001in the sky ✦
- Greedy Function Approximation: A Gradient Boosting Machine2001in the sky ✦
- LightGBM: A Highly Efficient Gradient Boosting Decision Tree2017in the sky ✦
- LightGBM: A Highly Efficient Gradient Boosting Decision Tree2017in the sky ✦
- Random Forests2001in the sky ✦
- A Unified Approach to Interpreting Model Predictions2017in the sky ✦
- XGBoost: A Scalable Tree Boosting System2016in the sky ✦