The document discusses decision tree learning and includes sections on what decision trees are, appropriate problems for decision trees, information gain, entropy, and an example decision tree. It is presented by Prof. Neeraj Bhargava and Pramod Singh Rathore from the Department of Computer Science at MDS University Ajmer, Rajasthan, India for a session on decision tree learning.

1. Prof. Neeraj Bhargava
Pramod Singh Rathore
Department of Computer Science
School of Engineering & System Sciences,
MDS University Ajmer, Rajasthan, India
1
Decision Tree Learning

2. Session Objectives
 What are Decision trees?
 Appropriate problems for DTrees
 Information gain ,
 Entropy
 Demo
2

3. What are Decision trees?
 A decision tree is a tree in which each branch node
represents a choice between a number of
alternatives, and each leaf node represents a
decision.
 A type of supervised learning algorithm.
3

4. 4

5. Example of Decision Tree
5

6. Example….Contd
6

7. What is Entropy
7

8. What is Information Gain
8

9. Assignment 1
 Decision tree representation,
 appropriate problems for decision tree learning,
 basic decision tree algorithm,
 hyperspace search in decision tree learning,
 issues in decision tree learning
9

10. Queries ????
10

11. 11