In this video we dive into the nitty-gritty details of the math behind XGBoost trees. We derive the equations for the Output Values from the leaves as well as the Similarity Score. Then we show how these general equations are customized for Regression or Classification by their respective Loss Functions. If you make it to the end, you will be approximately 22% smarter than you are now! :)
NOTE: This StatQuest assumes that you are already familiar with...
XGBoost Part 1: XGBoost Trees for Regression: [ Ссылка ]
XGBoost Part 2: XGBoost Trees for Classification: [ Ссылка ]
Gradient Boost Part 1: Regression Main Ideas: [ Ссылка ]
Gradient Boost Part 2: Regression Details:[ Ссылка ]
Gradient Boost Part 3: Classification Main Ideas: [ Ссылка ]
Gradient Boost Part 4: Classification Details: [ Ссылка ]
...and Ridge Regression: [ Ссылка ]
Also note, this StatQuest is based on the following sources:
The original XGBoost manuscript: [ Ссылка ]
The original XGBoost presentation: [ Ссылка ]
And the XGBoost Documentation: [ Ссылка ]
Last but not least, I want to extend a special thanks to Giuseppe Fasanella and Samuel Judge for thoughtful discussions and helping me understand the math.
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Corrections:
1:16 The Lambda should be outside of the square brackets.
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