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Ensemble Learning

Q. Top 20 Interview Questions on Ensemble Learning with detailed Answers (All free)
Q. What is a Decision Tree? Explain the concept and working of a Decision tree model
Q. What is Bagging? How do you perform bagging and what are its advantages?
Q. Explain the concept and working of the Random Forest model
Q. What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
Q. What are the advantages and disadvantages of Decision Tree model?
Q. What are the advantages and disadvantages of Random Forest?
Q. What are the advantages and disadvantages of a GBM model?
Q. How is Gradient Boosting different from Random Forest?
Q. What is the difference between Adaboost and Gradient boost?
Q. Distinguish between a Weak learner and a Strong Learner
Q. What are the key hyperparameters for a Random Forest model?
Q. GBM vs Random Forest: which algorithm should be used when?
Q. What is XGBoost? How does it improve upon standard GBM?
Q. What are the best ways to safeguard against overfitting a GBM?
Q. What are the key hyperparameters for a GBM model?
Q. Explain the difference between Entropy, Gini, and Information Gain
Q. What does Gradient in Gradient Boosted Trees refer to?
Q. Why is Random Forest a non-linear model? Why does it result in non-linear decision boundaries?
Q. What are the options for reporting feature importance from a decision-tree based model?
Q. How does pruning a tree work?
Q. What is the difference between Decision Trees, Bagging and Random Forest?
Q. What is CART?
Q. How does a decision tree create splits from continuous features?

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Explore Questions by Topics

Computer Vision (1)
Generative AI (2)
Machine Learning Basics (18)
Deep Learning (52)
- DL Basics (16)
- DL Architectures (17)
  - Feedforward Network / MLP (2)
  - Sequence models (6)
  - Transformers (9)
- DL Training and Optimization (17)
Natural Language Processing (27)
- NLP Data Preparation (18)
Supervised Learning (115)
- Regression (41)
  - Linear Regression (26)
  - Generalized Linear Models (9)
  - Regularization (6)
- Classification (70)
  - Logistic Regression (10)
  - Support Vector Machine (9)
  - Ensemble Learning (24)
  - Other Classification Models (9)
  - Classification Evaluations (9)
Unsupervised Learning (55)
- Clustering (37)
  - Distance Measures (9)
  - K-Means Clustering (9)
  - Hierarchical Clustering (3)
  - Gaussian Mixture Models (5)
  - Clustering Evaluations (6)
- Dimensionality Reduction (9)
Statistics (34)
Data Preparation (35)
- Feature Engineering (30)
- Sampling Techniques (5)

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Other Questions in Ensemble Learning

What are options to calibrate probabilities produced from the output of a classifier that does not produce natural probabilities?
What are the subtypes of Cross Validation?
What is Specificity?
Explain the concept and working of the Random Forest model
How does a learning curve give insight into whether the model is under- or over-fitting?
What is the difference between Discriminative and Generative models?

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