Shrinkage Methods
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Machine Learning
Linear Methods For Regression
Interview questions on Linear Methods For Regression.
33 questions
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Shrinkage Methods
Q2. What are the benefits of using shrinkage methods over subset selection methods?
Shrinkage Methods
Q3. Name some shrinkage methods?
Shrinkage Methods
Q4. What's the main purpose of L1 and L2 regularization in linear regression?
Shrinkage Methods
Q5. How do we estimate coefficients in Ridge regression?
Shrinkage Methods
Q6. Explain the effect of tuning parameter $\lambda$ in ridge regression cost function?
Shrinkage Methods
Q7. How can we determine optimal value of $\lambda$ ?
Shrinkage Methods
Q8. Suppose you fit a ordinary linear regression model over your data and you find it is under-fitting. Is it good idea to use Ridge or Lasso regression here?
Shrinkage Methods
Q9. How do L1 and L2 regularization affect the model's coefficients?
Shrinkage Methods
Q10. Can we use ridge regression for variable selection purpose?
Shrinkage Methods
Q11. Write the loss function involve in lasso regression?
Shrinkage Methods
Q12. Which regression technique leads to sparse models? Lasso or ridge regression?
Shrinkage Methods
Q13. Why is it that the lasso, unlike ridge regression, results in coefficient estimates that are exactly equal to zero?
Shrinkage Methods
Q14. List some advantages of using lasso regression over ridge regression?
Shrinkage Methods
Q15. What are the hyper-parameters associated with L1 and L2 regularization?
Shrinkage Methods
Q16. When would you choose L1 regularization over L2, and vice versa?
Shrinkage Methods
Q17. What is Elastic Net regularization, and how does it relate to L1 and L2 regularization?
Shrinkage Methods
Q18. How do you choose the optimal regularization strength (alpha) for L1 and L2 regularization?
Shrinkage Methods
Q19. What are the consequences of multi-collinearity?
Shrinkage Methods
Q20. How can you detect collinearity in a regression model?
Shrinkage Methods
Q21. How can you detect multi-collinearity in a regression model?
Shrinkage Methods
Q22. How can you address multi-collinearity?
Shrinkage Methods
Q23. Can you have perfect multi-collinearity?
Shrinkage Methods
Q24. How can we determine whether a dataset is high-dimensional or low-dimensional?
Shrinkage Methods
Q25. What is the issue of using least squares regression in high dimensional setting?
Shrinkage Methods
Q26. What happens to the Train MSE and Test MSE in a linear regression model if we add features that are completely unrelated to the response?
Shrinkage Methods
Q27. What is curse of dimensionality?
Shrinkage Methods
Q28. If I add a feature to my linear regression model, how will it affect the Train MSE and Test MSE?
Shrinkage Methods
Q29. Can we use lasso or ridge regression in high dimensional setup?
Shrinkage Methods
Q30. Can we use lasso or ridge regression in high dimensional setup? (Part 2)
Shrinkage Methods
Q31. [True/False] In the high-dimensional setting, the multicollinearity problem is extreme?
Shrinkage Methods
Q32. [True/False] In the high-dimensional setting, the multicollinearity problem is extreme? (Part 2)
Shrinkage Methods