Gradient Boosting vs Random Forest

In this video, we compare two popular ensemble methods, Gradient Boosting Decision Trees and Random Forest. Both algorithms are widely used in machine learning for improving the accuracy of models by combining several weaker models. We'll discuss the differences between these two algorithms, including their training processes, performance metrics, and strengths and weaknesses. By the end of the video, you'll have a solid understanding of the similarities and differences between Gradient Boosting Decision Trees and Random Forest, and when to use each algorithm depending on the task at hand. This knowledge can help you choose the best ensemble method for your own machine learning projects.

Tutorials:
[1] sefiks.com/2021/12/26/random-forest-vs-gradient-bo…
[2] sefiks.com/2017/11/19/how-random-forests-can-keep-…
[3] sefiks.com/2018/10/04/a-step-by-step-gradient-boos…
[4] sefiks.com/2018/10/29/a-step-by-step-gradient-boos…

Videos by Pressmaster from Pexels: pexels.com/@pressmaster

Please Subscribe! That's what keeps me going ► bit.ly/40NfIS7

Want more? Connect with me here:
Blog: sefiks.com/
Twitter: twitter.com/serengil
Instagram: www.instagram.com/serengil
Facebook: www.facebook.com/sefikscom
Linkedin: www.linkedin.com/in/serengil/

If you do like my videos, you can support my effort with your financial contributions on
Patreon: www.patreon.com/serengil?source=youtube
GitHub Sponsors: github.com/sponsors/serengil
Buy Me a Coffee: buymeacoffee.com/serengil