Maximal Margin Classifier
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Machine Learning
Support Vector Machines
Interview questions on Support Vector Machines.
39 questions
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Maximal Margin Classifier
Q2. What is a hyperplane?
Maximal Margin Classifier
Q3. Write generic expression of a p-dimensional hyperplane?
Maximal Margin Classifier
Q4. How to determine whether a point or a vector lies on a hyperplane?
Maximal Margin Classifier
Q5. How does maximal margin classifier work?
Maximal Margin Classifier
Q6. What is the main limitation of maximal margin classifier?
Maximal Margin Classifier
Q7. How can we overcome limitation of maximal margin classifier?
Maximal Margin Classifier
Q8. How can we overcome limitation of maximal margin classifier? (Part 2)
Maximal Margin Classifier
Q9. What is difference between Maximal margin classifier(MMC) and Support vector classifier(SVC)?
Maximal Margin Classifier
Q10. How does Support vector classifier(SVC) works?
Maximal Margin Classifier
Q11. What kind of information we get from slack variables $\epsilon$ in SVC?
Maximal Margin Classifier
Q12. How to interpret regularization parameter $C$ in SVC?
Maximal Margin Classifier
Q13. What are support vectors in SVC?
Maximal Margin Classifier
Q14. Explain bias-variance tradeoff in SVC?
Maximal Margin Classifier
Q15. Is SVC robust to outliers?
Maximal Margin Classifier
Q16. What is the main limitation of SVC?
Maximal Margin Classifier
Q17. Can we use feature space enlarging technique to solve non linear decision boundary problem with SVC?
Maximal Margin Classifier
Q18. What is support vector machine(SVM)?
Maximal Margin Classifier
Q19. What do you mean by kernel function in SVM?
Maximal Margin Classifier
Q20. What is advantage of using kernel trick over simply enlarging feature space using functions of the original features?
Maximal Margin Classifier
Q21. What are major drawbacks of kernel based machines?
Maximal Margin Classifier
Q22. Why kernel trick is popular in SVM?
Maximal Margin Classifier
Q23. Is SVM a linear model or non-linear model?
Maximal Margin Classifier
Q24. What is the kernel trick in Support Vector Machines (SVM), and how does it work?
Maximal Margin Classifier
Q25. How can we set up SVMs to work with multi class classification problems?
Maximal Margin Classifier
Q26. Is the SVM unique in its use of kernels to enlarge the feature space to accommodate non-linear class boundaries?
Maximal Margin Classifier
Q27. When should we use SVMs over logistic regression?
Maximal Margin Classifier
Q28. What is the main benefit of using logistic regression over SVM?
Maximal Margin Classifier
Q29. Given a true label $y = +1$ and a predicted score $\hat{y} = 0.5$, calculate the hinge loss for this classification example?
Support Vector Machines
Q30. SVM.
- What’s linear separation? Why is it desirable when we use SVM?
- How well would vanilla SVM work on this dataset?
- How well would vanilla SVM work on this dataset?
- How well would vanilla SVM work on this dataset?
Support Vector Regression
Q31. What is the main difference between Support Vector Machines (SVM) and Support Vector Regression (SVR)?
Support Vector Regression
Q32. What are the key components of SVR?
Support Vector Regression
Q33. What is the role of the ε-tube in SVR?
Support Vector Regression
Q34. How do you tune the hyperparameters in SVR?
Support Vector Regression
Q35. What is the significance of the regularization parameter ($C$) in SVR?
Support Vector Regression
Q36. How does SVR handle outliers in the data?
Support Vector Regression
Q37. What are the evaluation metrics used to assess the performance of SVR models?
Support Vector Regression
Q38. What are the advantages and disadvantages of SVR compared to other regression techniques?
Support Vector Regression