Machine Learning Random Forest with Python from Scratch - Structure

The video tutorial introduces the concept of random forests, explaining that a forest is a collection of trees. It details the structure of a tree, including roots, leaves, and stems, and how these components form a decision-making tree. The tutorial further explains decision and leaf nodes, and how to visually represent a random forest. The session concludes with a discussion on terminology and a preview of implementing a tree in Python.

What is the definition of a forest in the context of random forests?

Describe the components of a tree as mentioned in the text.

Explain the process of creating a tree and how it relates to creating a forest.

How does the tree make decisions based on the attributes of the data?

What is a leaf node and how is it different from a decision node?

What is a decision node and how does it function within a tree?

What will be the next steps in learning about trees in the context of random forests?