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May 19, 2020
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 For a complete index of all the StatQuest videos, check out: https://statquest.org/video-index/ If you'd like to support StatQuest, please consider... Patreon: https://www.patreon.com/statquest ...or... YouTube Membership: https://www.youtube.com/channel/UCtYL... ...a cool StatQuest t-shirt or sweatshirt (USA/Europe): teespring.com/stores/statquest (everywhere): https://www.redbubble.com/people/star... ...buying one or two of my songs (or go large and get a whole album!) https://joshuastarmer.bandcamp.com/ ...or just donating to StatQuest! https://www.paypal.me/statquest Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter: https://twitter.com/joshuastarmer

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