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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: • XGBoost Part 1 (of 4):...
XGBoost Part 2: XGBoost Trees for Classification: • XGBoost Part 2 (of 4):...
Gradient Boost Part 1: Regression Main Ideas: • Gradient Boost Part 1 ...
Gradient Boost Part 2: Regression Details: • Gradient Boost Part 2 ...
Gradient Boost Part 3: Classification Main Ideas: • Gradient Boost Part 3 ...
Gradient Boost Part 4: Classification Details: • Gradient Boost Part 4 ...
...and Ridge Regression: • Regularization Part 1:...
Also note, this StatQuest is based on the following sources:
The original XGBoost manuscript: arxiv.org/pdf/...
The original XGBoost presentation: homes.cs.washi...
And the XGBoost Documentation: xgboost.readth...
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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statquest.org/...
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Corrections:
1:16 The Lambda should be outside of the square brackets.
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