Decision Tree using ID3
Algorithm
Introduction and overview of
Decision Trees and ID3
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Decision tree and classification, Lecture notes of Data Warehousing
classification techniques and decision tree
Typology: Lecture notes
2025/2026
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Decision Tree using ID
Algorithm
Introduction and overview of Decision Trees and ID
Decision Trees
- Supervised learning method
- (^) • Decision nodes split data
- (^) • Leaf nodes represent outcomes
- (^) • Types: Classification and Regression Trees
Entropy
- Measures uncertainty/randomness
- (^) • Entropy = 0 → No uncertainty
- (^) • Higher entropy → More randomness
Information Gain
- Reduction in entropy after splitting
- (^) • Used to select the best attribute
- (^) • Attribute with highest IG becomes node
Building the Tree
- Initial entropy calculated from 9 Yes and 5 No
- Compute IG for all attributes
- Outlook has highest Information Gain
Final Decision Tree
Root Node: Outlook
- (^) Overcast → Yes
- (^) Sunny → Humidity
- (^) Rain → Wind