Decision Tree Algorithms MCQs

1. What is a decision tree algorithm used for?

a. Classification

b. Regression

c. Clustering

d. Dimensionality reduction


Answer: a. Classification


2. Which algorithm is commonly used to construct decision trees?

a. ID3

b. K-Means

c. DBSCAN

d. Linear Regression


Answer: a. ID3


3. Which attribute selection measure is used in the ID3 algorithm?

a. Gini Index

b. Entropy

c. Information Gain

d. Chi-square


Answer: c. Information Gain


4. What is the goal of a decision tree algorithm during training?

a. To maximize accuracy

b. To minimize error

c. To minimize impurity

d. To maximize precision


Answer: c. To minimize impurity


5. Which algorithm can handle both categorical and numerical features in decision trees?

a. CART

b. ID3

c. C4.5

d. Random Forest


Answer: a. CART


6. Which decision tree algorithm supports multi-class classification?

a. ID3

b. C4.5

c. CART

d. AdaBoost


Answer: b. C4.5


7. Which algorithm is an extension of the C4.5 algorithm?

a. ID3

b. CART

c. C5.0

d. Random Forest


Answer: c. C5.0


8. Which decision tree algorithm is based on the concept of binary splitting?

a. ID3

b. C4.5

c. CART

d. Random Forest


Answer: c. CART


9. Which algorithm can handle missing values in decision trees?

a. ID3

b. C4.5

c. CART

d. Random Forest


Answer: b. C4.5


10. Which decision tree algorithm can handle both classification and regression tasks?

a. ID3

b. C4.5

c. CART

d. Random Forest


Answer: c. CART


11. Which algorithm is used to prune decision trees to avoid overfitting?

a. ID3

b. C4.5

c. CART

d. Random Forest


Answer: c. CART


12. Which attribute selection measure is used in the C4.5 algorithm?

a. Gini Index

b. Entropy

c. Information Gain

d. Chi-square


Answer: b. Entropy


13. Which decision tree algorithm can handle continuous and discrete features without discretization?

a. ID3

b. C4.5

c. CART

d. Random Forest


Answer: c. CART


14. Which algorithm uses a cost-complexity pruning technique to create smaller decision trees?

a. ID3

b. C4.5

c. CART

d. Random Forest


Answer: c. CART


15. Which decision tree algorithm is an ensemble method that combines multiple decision trees?

a. ID3

b. C4.5

c. CART

d. Random Forest


Answer: d. Random Forest


16. Which algorithm uses a voting mechanism to make predictions in Random Forest?

a. Weighted voting

b. Majority voting

c. Weighted averaging

d. Median voting


Answer: b. Majority voting


17. Which algorithm is used to handle imbalanced class distributions in decision trees?

a. SMOTE

b. ADASYN

c. Synthetic Minority Over-sampling Technique

d. Random Over-sampling


Answer: c. Synthetic Minority Over-sampling Technique (SMOTE)


18. Which decision tree algorithm is based on the concept of boosting?

a. ID3

b. C4.5

c. CART

d. AdaBoost


Answer: d. AdaBoost


19. Which algorithm assigns weights to data points during the training process in AdaBoost?

a. Uniform weights

b. Decreasing weights

c. Increasing weights

d. Adaptive weights


Answer: d. Adaptive weights


20. Which decision tree algorithm is based on the concept of feature bagging?

a. ID3

b. C4.5

c. CART

d. Bagging


Answer: d. Bagging


21. Which algorithm is used to reduce overfitting in decision trees by randomly selecting a subset of features?

a. Random subspace method

b. Random feature selection

c. Random attribute bagging

d. Random feature bagging


Answer: a. Random subspace method


22. Which decision tree algorithm uses the concept of rule-based learning?

a. ID3

b. C4.5

c. RIPPER

d. CART


Answer: c. RIPPER


23. Which algorithm is used to handle class imbalance by adjusting the class weights in decision trees?

a. Class-weighted decision trees

b. Class-balanced decision trees

c. Class-imbalance adjustment trees

d. Class-adjusted decision trees


Answer: a. Class-weighted decision trees


24. Which decision tree algorithm is suitable for handling missing attribute values using surrogate splits?

a. ID3

b. C4.5

c. CART

d. M5


Answer: d. M5


25. Which algorithm uses the concept of multi-output decision trees for multi-label classification?

a. ID3

b. C4.5

c. CART

d. M4.5


Answer: d. M4.5


26. Which decision tree algorithm is suitable for handling skewed class distributions using stratified sampling?

a. ID3

b. C4.5

c. CART

d. Stratified decision trees


Answer: d. Stratified decision trees


27. Which algorithm uses the concept of local search in decision trees to improve accuracy?

a. Local Search Trees (LST)

b. Iterative Local Search Trees (ILST)

c. Best-First Search Trees (BFST)

d. Greedy Search Trees (GST)


Answer: b. Iterative Local Search Trees (ILST)


28. Which decision tree algorithm is suitable for handling continuous-valued attributes by creating binary splits?

a. ID3

b. C4.5

c. CART

d. CVFDT


Answer: c. CART


29. Which algorithm is used to handle time-series data using decision trees?

a. Time-Series Decision Trees (TSDT)

b. Sequential Decision Trees (SDT)

c. Temporal Decision Trees (TDT)

d. Time-Dependent Decision Trees (TDDT)


Answer: c. Temporal Decision Trees (TDT)


30. Which decision tree algorithm is based on the concept of reducing variance by combining predictions from multiple trees?

a. ID3

b. C4.5

c. CART

d. Random Forest


Answer: d. Random Forest