Gradient Boost Part 3 (of 4): Classification

This is Part 3 in our series on Gradient Boost. At long last, we are showing how it can be used for classification. This video gives focuses on the main ideas behind this technique. The next video in this series will focus more on the math and how it works with the underlying algorithm.

This StatQuest assumes that you have already watched Part 1:
https://youtu.be/3CC4N4z3GJc

...and it also assumed that you understand Logistic Regression pretty well. Here are the links for...

A general overview of Logistic Regression: https://youtu.be/yIYKR4sgzI8
how to interpret the coefficients: https://youtu.be/vN5cNN2-HWE
and how to estimate the coefficients: https://youtu.be/BfKanl1aSG0

Lastly, if you want to learn more about using different probability thresholds for classification, check out the StatQuest on ROC and AUC: https://youtu.be/xugjARegisk

For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/

This StatQuest is based on the following sources:

A 1999 manuscript by Jerome Friedman that introduced Stochastic Gradient Boost: https://statweb.stanford.edu/~jhf/ftp/stobst.pdf

The Wikipedia article on Gradient Boosting: https://en.wikipedia.org/wiki/Gradient_boosting

The scikit-learn implementation of Gradient Boosting: https://scikit-learn.org/stable/modules/ensemble.html#gradient-boosting

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Видео Gradient Boost Part 3 (of 4): Classification канала StatQuest with Josh Starmer