Gradient Boosting vs Random Forest скачать в хорошем качестве

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] https://sefiks.com/2021/12/26/random-... [2] https://sefiks.com/2017/11/19/how-ran... [3] https://sefiks.com/2018/10/04/a-step-... [4] https://sefiks.com/2018/10/29/a-step-... Videos by Pressmaster from Pexels: pexels.com/@pressmaster Please Subscribe! That's what keeps me going ► https://bit.ly/40NfIS7 Want more? Connect with me here: Blog: https://sefiks.com/ Twitter:   / serengil   Instagram:   / serengil   Facebook:   / sefikscom   Linkedin:   / serengil   If you do like my videos, you can support my effort with your financial contributions on Patreon: https://www.patreon.com/serengil?sour... GitHub Sponsors: https://github.com/sponsors/serengil Buy Me a Coffee: https://buymeacoffee.com/serengil