Classification Interview Questions
- What is classification, and discuss the different types of classification?
- What are some common classification algorithms?
- How do you evaluate the performance of a classification model?
- What is a ROC curve?
- How do you handle imbalanced datasets in classification tasks?
- Explain the difference between Gini, Entropy, and Information Gain
- What is Logistic Regression? Describe the entire process of using logistic regression to fit data
- What are the major assumptions of logistic regression?
- What are the advantages and disadvantages of logistic regression?
- What is the basic idea of Support Vector Machine (SVM) and Maximum Margin?
- What hyper-parameters are typically tuned in SVM?
- What are the pros/cons of using an SVM model?
- What are common choices to use for kernels in SVM?
- Describe the hinge loss function used in SVM
- What is the kernel trick in SVM?
- 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 is XGBoost? How does it improve upon standard GBM?
- Distinguish between a Weak learner and a Strong Learner
- What parameters can be tweaked for a Random Forest model? Explain in detail
- GBM vs Random Forest: which algorithm should be used when?
- What is the difference between a generative and a discriminative model?
- What is a naive bayes classifier? Explain how does Naive Bayes work
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