Machine Learning: Random Forest with Python from Scratch - Leaf and Decision Node

The video tutorial explains the creation of two classes: a leaf node and a decision node (DCN node). The leaf node is designed to make predictions based on class counts, while the DCN node is structured to have a question, true branch, and false branch. The tutorial outlines the process of writing constructors for these nodes and emphasizes that the build tree method will integrate these components. The build tree method will be concise, leveraging previously written helper functions. The video concludes with a note that the implementation of the build tree method will be covered in the next lecture.

What is the primary function of a leaf node in a decision tree?

• To split data into two branches

• To make predictions based on class counts

• To combine multiple nodes into a tree

• To ask a question for further branching

How does a DCN node differ from a leaf node?

• It does not make predictions

• It has a question and two branches

• It is the final node in a tree

• It only handles one type of data

What are the components of a DCN node?

• A question, a true branch, and a false branch

• A prediction and a class count

• A single branch and a label

• A root and a leaf

What is the purpose of the build tree method?

• To define the structure of a leaf node

• To implement the logic for a DCN node

• To combine functions and build a complete tree

• To predict outcomes based on input data

What will the build tree method return once implemented?

• A set of questions for the DCN nodes

• A list of predictions

• A pointer to the root of the tree

• A single leaf node