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Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)

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Ensemble Learning Interview Questions

(Decision Trees, Bagging, Boosting, Random Forest)

  1. What is a Decision Tree? Explain the concept and working of a Decision tree model
  2. What is Bagging? How do you perform bagging and what are its advantages?
  3. Explain the concept and working of the Random Forest model
  4. What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
  5. What are the key hyperparameters for a GBM model?
  6. What are the key hyperparameters for a Random Forest model?
  7. Explain the difference between Entropy, Gini, and Information Gain
  8. How would you evaluate a classification model?
  9. What is XGBoost? How does it improve upon standard GBM?
  10. How is Gradient Boosting different from Random Forest?
  11. What is the difference between Adaboost and Gradient boost?
  12. Distinguish between a Weak learner and a Strong Learner 
  13. GBM vs Random Forest: which algorithm should be used when?
  14. What are the advantages and disadvantages of Decision Tree model?
  15. What are the advantages and disadvantages of Random Forest?
  16. What are the advantages and disadvantages of a GBM model?
  17. What are the best ways to safeguard against overfitting a GBM?
  18. What does Gradient in Gradient Boosted Trees refer to?
  19. What is the difference between Decision Trees, Bagging and Random Forest?
  20. How does pruning a tree work?

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