Machine Learning Random Forest with Python from Scratch - How to Build a Tree

The video tutorial explains how to build a decision tree using a recursive approach. It covers the best split function to determine the optimal question for splitting data, the role of leaf nodes in making predictions, and the use of recursion to construct the tree. The tutorial also discusses the partition method for dividing data into true and false rows and concludes with the implementation of a custom fit method for training the model.

How does recursion play a role in the 'build tree' method?

What is the significance of the decision node in the tree structure?

How does the implementation of the tree relate to the random forest classifier?