Understanding Gradient Boosting and XGBoost in Decision Trees

Dive into the world of Gradient Boosting and XGBoost techniques with a focus on Decision Trees, their applications, optimization, and training methods. Explore the significance of parameter tuning and training with samples to enhance your machine learning skills. Access resources to deepen your understanding and stay updated on the latest advancements in the field.

• Gradient Boosting
• XGBoost
• Decision Trees
• Machine Learning
• Parameter Tuning

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Uploaded on Aug 14, 2024 | 1 Views

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1. Gradient B Boosting Decision Tree/eXtremeGradient Boosting 2021.12.29 Jialin Li

2. Boosting Y=f(x)+L L:redusial function Samples are strongly relative.

3. CART :

4. Gradient Boosting Decision Tree fM(x)= m=1 ?m(?) , Tm is the m tree m is number of trees. M

5. XGboost optimized GBDT 1.L 2. L 3.

6. Application Import xgboost 1. 2. (train and test) 3. Some examples https://github.com/dmlc/xgboost

7. Next to do 1. Try to train with our samples 2. Learn the meaning of parameters (important to adjust the params)

8. Backup GBDT

9. Backup About XGboost https://indico.cern.ch/event/382895/contributions/910921/attachments/763480/104 7450/XGBoost_tianqi.pdf