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

The video tutorial explains how to build a decision tree by first identifying the best question to ask at each node using a function called 'best split'. It describes the role of leaf nodes, which contain predictions based on class counts. The tutorial covers the recursive process of tree construction, detailing how to handle true and false branches. It concludes with a discussion on using the constructed tree and hints at writing a custom fit method for model training, similar to a random forest classifier.

How does the recursive call to 'buildtree' function work with true and false rows?

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

What steps are involved in using the tree after it has been built?