Decision Tree Characteristics
Decision Trees Characteristics Context: Decision Trees are a fundamental machine learning algorithm used for both classification and regression tasks. Understanding their characteristics, capabilities, and limitations is crucial for effectively applying them to solve real-world problems. Question: Which of the following statements are true regarding the properties and behavior of Decision Trees? Statements to Evaluate: 1. Decision tree makes no assumptions about the data. 2. The decision tree model can learn non-linear decision boundaries. 3. Decision trees cannot explain how the target will change if a variable is changed by 1 unit (marginal effect). 4. Hyperparameter tuning is not required in decision trees. 5. In a decision tree, increasing entropy implies increasing purity. 6. In a decision tree, the entropy of a node decreases as we go down the decision tree. Choose the correct answer from below : A) 1, 2, and 5 B) 3, 5 and 6 C) 2, 3, 4 and 5 D) 1,2,3 and 6 Ans: D 1, 2, 3 and 6