Welcome to the Machine Learning Course for Black and Indigenous Students!

This program is offered by Vector Institute in its drive to build research and expand career pathways in the field of AI for under-represented populations.

Instructor: Bonaventure Molokwu | Tutorial Developer: Manmeet Kaur Baxi | Course Tutors: Yinka Oladimeji and Manmeet Kaur Baxi | Course Director: Shingai Manjengwa (@Tjido)

Never stop learning!

Decision Trees

• Decision trees are predictive models that use a set of binary rules to calculate a target value.
• Each individual tree is a fairly simple model that has branches, nodes and leaves.
• A type of supervised learning algorithm, used in both regression and classification problems, but mostly for classification.
• Works for both categorical and continuous input and output variables.

Important Terminology

1. Root Node: It represents the entire population or sample and this further gets divided into two or more homogeneous sets.
2. Splitting: It is a process of dividing a node into two or more sub-nodes.
3. Decision Node: When a sub-node splits into further sub-nodes, then it is called a decision node.
4. Leaf/Terminal Node: Nodes that do not split is called Leaf or Terminal node.
5. Pruning: When we remove sub-nodes of a decision node, this process is called pruning. You can say the opposite process of splitting.
6. Branch / Sub-Tree: A subsection of the entire tree is called a branch or sub-tree.
7. Parent and Child Node: A node, which is divided into sub-nodes is called the parent node of sub-nodes whereas sub-nodes are the child of a parent node.

Types of Decision Trees:

Regression Trees: