DT Quiz

Question 1: Which of the following is a key concept in decision tree classifiers?

• Gradient Descent 

• Decision Matrix 

• Entropy 

• Normalization 

Question 2: In a decision tree, what is the purpose of the root node?

• Make final predictions 

• Split the dataset into subsets 

• Prune unnecessary branches 

• Evaluate model accuracy 

Question 3: What criterion is commonly used to measure impurity in a decision tree node?

• Mean Squared Error 

• Information gain 

• Pearson Correlation 

• Cross-Entropy 

Question 4: Which technique helps prevent overfitting in decision tree models?

• Feature Scaling 

• Pruning

• Normalization 

• Data Augmentation 

Question 5: What is the primary purpose of a decision tree classifier?

• Regression analysis 

• Dimensionality reduction 

• Classification 

• Clustering 

Question 6 :How does a decision tree make decisions at each internal node?

• Randomly 

• Based on majority voting 

• By maximizing information gain or minimizing impurity 

• Using a neural network 

Question 7: What is the potential drawback of creating a deep decision tree?

• Underfitting 

• Bias 

• Overfitting 

• Poor accuracy 

Question 8: How should the splitting procedure stop?

• All samples for a given node belong to the different classes

• No samples left

• Many remaining attributes for further partitioning   

• None of the above