1. What are ensemble methods mainly used for?
(A) Data cleaning
(B) Improving model performance by combining models
(C) Feature selection
(D) Data visualization
2. Which ensemble technique builds models sequentially?
(A) Bagging
(B) Boosting
(C) Stacking
(D) Random Forest
3. Which ensemble method reduces variance?
(A) Boosting
(B) Stacking
(C) Bagging
(D) AdaBoost
4. Which algorithm is an example of bagging?
(A) AdaBoost
(B) Gradient Boosting
(C) XGBoost
(D) Random Forest
5. Which ensemble method focuses more on misclassified instances?
(A) Bagging
(B) Voting
(C) Stacking
(D) Boosting
6. What does bagging stand for?
(A) Bayesian Aggregation
(B) Balanced Grouping
(C) Bootstrap Aggregating
(D) Batch Aggregation
7. Which technique combines predictions using a meta-model?
(A) Bagging
(B) Boosting
(C) Stacking
(D) Random Forest
8. Which ensemble method uses majority voting?
(A) Stacking
(B) Bagging
(C) Voting classifier
(D) Boosting
9. Which ensemble technique reduces bias?
(A) Bagging
(B) Random Forest
(C) Boosting
(D) Voting
10. Which algorithm is a boosting method?
(A) KNN
(B) Naive Bayes
(C) Linear Regression
(D) AdaBoost
11. Which ensemble method builds decision trees on random feature subsets?
(A) Bagging
(B) Random Forest
(C) Boosting
(D) Stacking
12. Which ensemble method is most prone to overfitting noisy data?
(A) Bagging
(B) Voting
(C) Random Forest
(D) Boosting
13. Which ensemble technique uses weighted voting?
(A) Bagging
(B) Simple voting
(C) Random Forest
(D) Boosting
14. Which ensemble method combines different types of models?
(A) Bagging
(B) Stacking
(C) Boosting
(D) Random Forest
15. Which ensemble technique is best for reducing overfitting?
(A) Boosting
(B) Bagging
(C) Stacking
(D) AdaBoost
16. Which ensemble algorithm is based on gradient descent?
(A) Gradient Boosting
(B) AdaBoost
(C) Random Forest
(D) Bagging
17. Which ensemble method combines weak learners into a strong learner?
(A) Bagging
(B) Boosting
(C) Voting
(D) Random Forest
18. Which ensemble technique is also known as parallel learning?
(A) Boosting
(B) Stacking
(C) Bagging
(D) Voting
19. Which ensemble method is commonly used in competitions?
(A) XGBoost
(B) Linear Regression
(C) K-Means
(D) PCA
20. Which statement about ensemble methods is TRUE?
(A) They always reduce bias
(B) They use only one model
(C) They eliminate the need for training data
(D) They can improve prediction accuracy