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NSCT-Ensemble Learning MCQs

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1. . Ensemble learning is:





2. . The main goal of ensemble learning is to:





3. . Bagging in ensemble learning stands for:





4. . Bagging improves model performance by:





5. . Random Forest is:





6. . Boosting is:





7. . AdaBoost stands for:





8. . Gradient Boosting works by:





9. . Stacking in ensemble learning is:





10. . Voting classifiers in ensemble learning:





11. . Ensemble methods are used to:





12. . Key advantage of ensemble learning is:





13. . Random Forest handles overfitting by:





14. . Difference between bagging and boosting is:





15. . Out-of-bag (OOB) error is used in:





16. . Ensemble methods are particularly useful when:





17. . Weighted voting in ensemble learning:





18. . Bagging reduces:





19. . Boosting reduces:





20. . The main purpose of ensemble learning is to:





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