Machine Learning: Random Forest with Python from Scratch - Structure

The video tutorial introduces the concept of a random forest, explaining that it is a collection of decision trees. It details the structure of a tree, including root, leaf, and decision nodes, and how these components work together to make decisions. The tutorial provides a step-by-step guide on creating a decision tree, using examples to illustrate the decision-making process. It concludes by preparing viewers for the next session, where they will learn to implement a tree in Python using a Jupyter Notebook.

What new insight or understanding did you gain from this video?