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 a forest in the context of random forests?

• A collection of trees

• A single tree

• A type of plant

• A single leaf

Why is it important to understand how to create a single tree?

• Because it is unrelated to forests

• Because it is a fun activity

• Because it is the foundation for creating a forest

• Because it helps in drawing better

In a decision tree, what is the role of the root node?

• It is the final decision point

• It is where the decision-making process starts

• It is a decorative element

• It is the same as a leaf node

What happens if the color is not green in the decision tree example?

• It is automatically an apple

• The diameter is checked next

• The process ends

• It is considered a lemon

What is a leaf node in a decision tree?

• A node that has no further branches

• A node that leads to another decision

• A node that is at the top

• A node that is always false

Which node is responsible for making decisions in a tree?

• Root node

• Decision node

• Leaf node

• Stem node

What is the significance of a decision node?

• It is always at the bottom

• It is a decorative element

• It is the end of the tree

• It is where decisions are made based on conditions