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