Linear Regression
← Question BankSign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark Sign in to bookmark
Machine Learning
Linear Methods For Regression
Interview questions on Linear Methods For Regression.
62 questions
Sign in to bookmark
Linear Regression
Q2. How to determine the coefficients of a simple linear regression model?
Linear Regression
Q3. In which scenarios linear model can outperforms fancier non linear models?
Linear Regression
Q4. Suppose a model takes form of $f(X) = \beta_{0} + \beta_{1}X_{1} + \beta_{2}X_{1}^{2}....$, Is it a linear model?
Linear Regression
Q5. What are the assumptions of linear regression?
Linear Regression
Q6. Explain the difference between simple linear regression and multiple linear regression.
Linear Regression
Q7. What is Residual Standard Error(RSE) and how to interpret it?
Linear Regression
Q8. What is the purpose of the coefficient of determination (R-squared) in linear regression?
Linear Regression
Q9. How to interpret the values of $R^2$ statistic?
Linear Regression
Q10. How do you interpret the coefficients in a linear regression model?
Linear Regression
Q11. What is the difference between correlation and regression?
Linear Regression
Q12. What are the methods to assess the goodness of fit of a linear regression model?
Linear Regression
Q13. What is the purpose of the F-statistic in linear regression?
Linear Regression
Q14. What are the potential problems in linear regression analysis, and how can you address them?
Linear Regression
Q15. What are some regularization techniques used in linear regression, and when are they applicable?
Linear Regression
Q16. Can you explain the concept of bias-variance trade-off in the context of linear regression?
Subset Selection
Q17. What do you mean by subset selection and how it is useful in linear regression models?
Subset Selection
Q18. What are some methods for selecting a subset of predictors?
Subset Selection
Q19. Explain Best Subset Selection method?
Subset Selection
Q20. What is the drawback of selecting the best subset of features on the basis of Residual Square Error(RSS) or $R^2$ score in the above method?
Subset Selection
Q21. What are the limitations of Best Subset Selection method?
Subset Selection
Q22. Given a use case that necessitates building a predictive model with a large number of features/predictors, which feature selection method would be most appropriate?
Subset Selection
Q23. Why Forward Stepwise Selection method is better than the Best Subset Selection?
Subset Selection
Q24. How does the forward stepwise selection method works?
Subset Selection
Q25. What is the major issue with stepwise selection?
Subset Selection
Q26. Imagine you have a dataset with $100$ observations $(n=100)$ and $1000$ predictors $(p=1000)$. Which feature selection method you can use?
Subset Selection
Q27. Explain Backward stepwise selection method?
Subset Selection
Q28. One issue with Backward stepwise selection method?
Subset Selection
Q29. Is it good idea to combine forward and backward stepwise subset selection technique as a single method?
Shrinkage Methods
Q30. What is shrinkage methods?
Shrinkage Methods
Q31. What are the benefits of using shrinkage methods over subset selection methods?
Shrinkage Methods
Q32. Name some shrinkage methods?
Shrinkage Methods
Q33. What's the main purpose of L1 and L2 regularization in linear regression?
Shrinkage Methods
Q34. How do we estimate coefficients in Ridge regression?
Shrinkage Methods
Q35. Explain the effect of tuning parameter $\lambda$ in ridge regression cost function?
Shrinkage Methods
Q36. How can we determine optimal value of $\lambda$ ?
Shrinkage Methods
Q37. 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
Q38. How do L1 and L2 regularization affect the model's coefficients?
Shrinkage Methods
Q39. Can we use ridge regression for variable selection purpose?
Shrinkage Methods
Q40. Write the loss function involve in lasso regression?
Shrinkage Methods
Q41. Which regression technique leads to sparse models? Lasso or ridge regression?
Shrinkage Methods
Q42. Why is it that the lasso, unlike ridge regression, results in coefficient estimates that are exactly equal to zero?
Shrinkage Methods
Q43. List some advantages of using lasso regression over ridge regression?
Shrinkage Methods
Q44. What are the hyper-parameters associated with L1 and L2 regularization?
Shrinkage Methods
Q45. When would you choose L1 regularization over L2, and vice versa?
Shrinkage Methods
Q46. What is Elastic Net regularization, and how does it relate to L1 and L2 regularization?
Shrinkage Methods
Q47. How do you choose the optimal regularization strength (alpha) for L1 and L2 regularization?
Shrinkage Methods
Q48. What are the consequences of multi-collinearity?
Shrinkage Methods
Q49. How can you detect collinearity in a regression model?
Shrinkage Methods
Q50. How can you detect multi-collinearity in a regression model?
Shrinkage Methods
Q51. How can you address multi-collinearity?
Shrinkage Methods
Q52. Can you have perfect multi-collinearity?
Shrinkage Methods
Q53. How can we determine whether a dataset is high-dimensional or low-dimensional?
Shrinkage Methods
Q54. What is the issue of using least squares regression in high dimensional setting?
Shrinkage Methods
Q55. 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
Q56. What is curse of dimensionality?
Shrinkage Methods
Q57. If I add a feature to my linear regression model, how will it affect the Train MSE and Test MSE?
Shrinkage Methods
Q58. Can we use lasso or ridge regression in high dimensional setup?
Shrinkage Methods
Q59. Can we use lasso or ridge regression in high dimensional setup? (Part 2)
Shrinkage Methods
Q60. [True/False] In the high-dimensional setting, the multicollinearity problem is extreme?
Shrinkage Methods
Q61. [True/False] In the high-dimensional setting, the multicollinearity problem is extreme? (Part 2)
Shrinkage Methods