Ensemble Learning Interview Questions
(Decision Trees, Bagging, Boosting, Random Forest)
- What is a Decision Tree? Explain the concept and working of a Decision tree model
- What is Bagging? How do you perform bagging and what are its advantages?
- Explain the concept and working of the Random Forest model
- What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
- What are the key hyperparameters for a GBM model?
- What are the key hyperparameters for a Random Forest model?
- Explain the difference between Entropy, Gini, and Information Gain
- How would you evaluate a classification model?
- What is XGBoost? How does it improve upon standard GBM?
- How is Gradient Boosting different from Random Forest?
- What is the difference between Adaboost and Gradient boost?
- Distinguish between a Weak learner and a Strong Learner
- GBM vs Random Forest: which algorithm should be used when?
- What are the advantages and disadvantages of Decision Tree model?
- What are the advantages and disadvantages of Random Forest?
- What are the advantages and disadvantages of a GBM model?
- What are the best ways to safeguard against overfitting a GBM?
- What does Gradient in Gradient Boosted Trees refer to?
- What is the difference between Decision Trees, Bagging and Random Forest?
- How does pruning a tree work?
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