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‘Regression’ Interview Questions

What is Regression?

Differentiate between Linear Models and Non Linear Models

What is Linear Regression?

What are the assumptions of linear regression?

How are coefficients of linear regression estimated?

What are some of the evaluation criteria used to assess a Linear Regression model?

What is Regularization?

What does L1 regularization (Lasso) mean?

What does L2 regularization (Ridge) mean?

When to use Ridge Regression vs Lasso?

What is Elastic-net? Why is it better in comparison to Ridge and Lasso?

What is R-squared and adjusted R-squared?

What are the various measures of error (MSE, RMSE, MAE)?

What is Global F-Test?

What is Information Criteria (AIC, BIC)?

What are potential problems encountered in Linear Regression?

What are some methods of Variable Selection?

Suppose there are a large number of predictors ‘p’. What is the best approach to find out if any of the p predictors are helpful in predicting the response ‘y’?

How would you perform feature selection using Lasso?

What are some of the problems with stepwise selection approaches?

How is variability measured in Linear Regression?

What is multicollinearity and how can that be identified?

Why does multicollinearity result in poor estimates of coefficients in linear regression?

How can qualitative predictors be incorporated in linear regression?

What are the most common transformations when the target variable is not normally distributed?

What are some approaches for modeling non linear relationships?

Doesn’t polynomial regression violate the multicollinearity assumption for Linear Regression?

What is the difference between Regression and ANOVA?

What is non-negative least squares, and when is it used?

What is an outlier?

What is a high leverage point?

What is a high influence point?

What is the difference between outliers, high leverage points, and high influence points?

What is a Generalized Linear Model (GLM)?

Briefly discuss other models that fall within the scope of GLM.

How does GLM adjust to the case of count data?

What is the cost function used in Poisson Regression?

What is overdispersion in Poisson Regression, and what are alternate specifications for when it is present?

What about cases where a significant number of observations have a count of 0 (in the context of Poisson Regression)?

What is Gamma Regression?

What is Beta regression?

What is Tweedie Regression?

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‘Regression’ Interview Questions