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Top 25 Interview Questions on Classification with detailed Answers (All free)

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Top 25 Interview Questions on Classification

Classification Interview Questions

  1. What is classification, and discuss the different types of classification?
  2. What are some common classification algorithms?
  3. How do you evaluate the performance of a classification model?
  4. What is a ROC curve?
  5. How do you handle imbalanced datasets in classification tasks?
  6. Explain the difference between Gini, Entropy, and Information Gain
  7. What is Logistic Regression? Describe the entire process of using logistic regression to fit data
  8. What are the major assumptions of logistic regression?
  9. What are the advantages and disadvantages of logistic regression?
  10. What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?
  11. What hyper-parameters are typically tuned in SVM?
  12. What are the pros/cons of using an SVM model?
  13. What are common choices to use for kernels in SVM?
  14. Describe the hinge loss function used in SVM
  15. What is the kernel trick in SVM?
  16.  What is a Decision Tree? Explain the concept and working of a Decision tree model
  17.  What is Bagging? How do you perform bagging and what are its advantages?
  18.  Explain the concept and working of the Random Forest model
  19.  What is Gradient Boosting (GBM)? Describe how does the Gradient Boosting algorithm work
  20.  What is XGBoost? How does it improve upon standard GBM?
  21.  Distinguish between a Weak learner and a Strong Learner
  22.  What parameters can be tweaked for a Random Forest model? Explain in detail 
  23.  GBM vs Random Forest: which algorithm should be used when?
  24. What is the difference between a generative and a discriminative model?
  25. What is a naive bayes classifier? Explain how does Naive Bayes work

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