Algorithm: Boosting model with XGBoost
Difference between bagging and boosting: We call each sub model in ensemble mode as weak learner. In random forest, it is the decision tree. Weak Learner: it can't be used to predict the result indepe
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相關文章
- 1. Tree - XGBoost with parameter description
- 2. Boosting Ensemble and GBDT Algorithm
- 3. XGBoost Algorithm: Long May She Reign!
- 4. Aggregation Model : Blending , Bagging , Boosting
- 5. Xgboost - A scalable tree boosting system Chiang
- 6. 集成學習之Boosting —— XGBoost
- 7. 提升算法-boosting algorithm
- 8. 『Gradient Boosting 』LightGBM與XGBoost對比
- 9. Parallel Gradient Boosting Decision Trees
- 10. XGBoost: A Scalable Tree Boosting System（XGBoost：一個可擴展的樹提升系統）