XGBoost-Part-4-Crazy-Cool-Optimizations - CrossMinds.ai
May 19, 20204 views
StatQuest with Josh Starmer
This video covers all kinds of extra optimizations that XGBoost uses when the training dataset is huge. So we'll talk about the Approximate Greedy Algorithm, Parallel Learning, The Weighted Quantile Sketch, Sparsity-Aware Split Finding (i.e. how XGBoost deals with missing data and uses default paths), Cache-Aware Access and Blocks for Out-of-Core Computation. That's a lot of stuff, but we'll go through it step-by-step and it will be a whole lot of fun. :)

NOTE: This StatQuest assumes that you are already familiar with...

XGBoost Part 1: XGBoost Trees for Regression: https://youtu.be/OtD8wVaFm6E
XGBoost Part 2: XGBoost Trees for Classification: https://youtu.be/8b1JEDvenQU
Quantiles and Percentiles: https://youtu.be/IFKQLDmRK0Y

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