Machine Learning - XGBoost Part 2 (of 4): Classification

In this video we pick up where we left off in part 1 and cover how XGBoost trees are built for Classification. NOTE: This StatQuest assumes that you are already familiar with... XGBoost Part 1: XGBoost Trees for Regression: https://youtu.be/OtD8wVaFm6E ...the main ideas behind Gradient Boost for Classification: https://youtu.be/jxuNLH5dXCs ...Odds and Log(odds): https://youtu.be/ARfXDSkQf1Y ...and how the Logistic Function works: https://youtu.be/BfKanl1aSG0 Also note, this StatQuest is based on the following sources: The original XGBoost manuscript: https://arxiv.org/pdf/1603.02754.pdf The original XGBoost presentation: https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf And the XGBoost Documentation: https://xgboost.readthedocs.io/en/latest/index.html 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/UCtYLUTtgS3k1Fg4y5tAhLbw/join ...buying one of my books, a study guide, a t-shirt or hoodie, or a song from the StatQuest store... https://statquest.org/statquest-store/ ...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 Corrections: 14:24 I meant to say "larger" instead of "lower. 18:48 In the original XGBoost documents they use the epsilon symbol to refer to the learning rate, but in the actual implementation, this is controlled via the "eta" parameter. So, I guess to be consistent with the original documentation, I made the same mistake! :) #statquest #xgboost