Decision Tree Learning

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

2. Session Objectives
 What are Decision trees?
 Appropriate problems for DTrees
 Information gain ,
 Entropy
 Demo
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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.
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5. Example of Decision Tree
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6. Example….Contd
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7. What is Entropy
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8. What is Information Gain
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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
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10. Queries ????
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