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