NSCT-Ensemble Learning MCQs 20 min Score: 0 Attempted: 0/20 Subscribe 1. . Ensemble learning is: (A) Encrypting models (B) A machine learning technique that combines multiple models to improve overall performance (C) Compressing datasets (D) Backup onlyShow All Answers 2. . The main goal of ensemble learning is to: (A) Reduce errors, variance, and bias compared to individual models (B) Encrypt predictions (C) Compress predictions (D) Backup only 3. . Bagging in ensemble learning stands for: (A) Balanced Algorithm (B) Binary Aggregation (C) Bootstrap Aggregating (D) Backup only 4. . Bagging improves model performance by: (A) Compressing predictions (B) Encrypting models (C) Training multiple models on random subsets of data and averaging predictions (D) Backup only 5. . Random Forest is: (A) Encrypting trees (B) An ensemble of decision trees using bagging and feature randomness (C) Compressing trees (D) Backup only 6. . Boosting is: (A) Backup only (B) Encrypting boosting (C) Compressing boosting (D) An ensemble method that trains models sequentially, giving more weight to previously misclassified instances 7. . AdaBoost stands for: (A) Adaptive Boosting (B) Automatic Boosting (C) Algorithmic Boosting (D) Backup only 8. . Gradient Boosting works by: (A) Encrypting gradient (B) Optimizing a loss function by sequentially adding models to correct errors (C) Compressing gradients (D) Backup only 9. . Stacking in ensemble learning is: (A) Compressing predictions (B) Encrypting stacks (C) Combining predictions of multiple models using a meta-model (D) Backup only 10. . Voting classifiers in ensemble learning: (A) Encrypt votes (B) Make predictions based on majority voting from multiple models (C) Compress votes (D) Backup only 11. . Ensemble methods are used to: (A) Compress datasets (B) Encrypt models (C) Increase accuracy and robustness of machine learning models (D) Backup only 12. . Key advantage of ensemble learning is: (A) Encrypting predictions (B) Reducing overfitting and improving generalization (C) Compressing features (D) Backup only 13. . Random Forest handles overfitting by: (A) Using multiple decision trees and averaging their predictions (B) Encrypting trees (C) Compressing trees (D) Backup only 14. . Difference between bagging and boosting is: (A) Bagging encrypts data (B) Bagging trains models in parallel, boosting trains sequentially (C) Boosting compresses features (D) Backup only 15. . Out-of-bag (OOB) error is used in: (A) Encrypting error (B) Random Forest to estimate prediction error without separate test data (C) Compressing OOB data (D) Backup only 16. . Ensemble methods are particularly useful when: (A) Backup only (B) Encrypting models (C) Compressing datasets (D) Individual models have high variance or bias 17. . Weighted voting in ensemble learning: (A) Assigns different importance to each model's prediction (B) Encrypts weights (C) Compresses votes (D) Backup only 18. . Bagging reduces: (A) Data size (B) Bias (C) Variance of model predictions (D) Backup only 19. . Boosting reduces: (A) Backup only (B) Variance only (C) Data size (D) Bias and improves model accuracy 20. . The main purpose of ensemble learning is to: (A) Encrypt all models (B) Combine multiple models to create a stronger, more accurate, and robust predictive model (C) Compress all features (D) Backup only
