MODULE 2 – DECISION TREE LEARNING 1. What is decision tree and decision tree learning? 2. Explain representation of decision tree with example. 3. What are appropriate problems for Decision tree learning? 4. Explain the concepts of Entropy and Information gain. 5. Describe the ID3 algorithm for decision tree learning with example 6. Give Decision trees to represent the Boolean Functions: a) A && ~ B b) A V [B && C] c) A XOR B d) [A&&B] V [C&&D] 7. Give Decision trees for the following set of training examples Day Outlook Temperature Humidity ...