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-forest-vs-gradient-boosting/
[2] https://sefiks.com/2017/11/19/how-random-forests-can-keep-you-from-decision-tree/
[3] https://sefiks.com/2018/10/04/a-step-by-step-gradient-boosting-decision-tree-example/
[4] https://sefiks.com/2018/10/29/a-step-by-step-gradient-boosting-example-for-classification/

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