Machine Learning Mock Interview

Practice 20 Machine Learning interview questions covering algorithms, model evaluation, and deep learning fundamentals.

Question 16 of 20

Please discuss the differences between generative and discriminative models?

How to Answer
Example Answer
Community Answers

William Swansen has worked in the employment assistance realm since 2007. He is an author, job search strategist, and career advisor who helps individuals worldwide and in various professions to find their ideal careers.

You probably already recognize this as a technical question. Questions asking you to discuss the differences between two concepts or terms used in your profession are easily identified as technical. The best way to respond to a technical question is to first define the terms, then either compare them or discuss how they are used in your work. You may also want to provide some examples relevant to the organization you are interviewing with to further demonstrate your knowledge in this area.

William Swansen

"Both generative and discriminative models are related to categories of data. A generative model is designed to learn the categories of the data in a study, while the discriminative model seeks only to learn the distinction between the categories. In machine learning, discriminative models outperform generative ones when conducting classification exercises."

Write Your Answer

0 - Character Count

Previous Question Next Question

20Machine Learning Interview Questions & Answers

General

1.In your opinion, what is the most valuable data applicable to our business?

Operational

2.What steps would you use to create and implement a data-based decision-making system for our company's users?

Technical

3.Can you briefly discuss the trade-offs between bias and variance?

Technical

4.Can you discuss the difference between supervised and unsupervised machine learning and when each one is used?

Technical

5.What is the difference between K-Nearest Neighbors and k-means clustering?

Discovery

6.Can you talk about how precision and recall are used in the work you do?

Technical

7.Please discuss how a ROC curve works.

Technical

8.Can you define Bayes' Theorem and discuss how it is useful in the context of machine learning?

Technical

9.Bayes' Theorem is often described as 'Naive.' Why is Bayes naive?

Technical

10.Please discuss the purpose of regularization and explain the difference between L2 and L1 regularization.

Discovery

11.Do you have a â€˜go-to' algorithm, and can you describe it to me?

Technical

12.What's the difference between the concepts of probability and likelihood?

Technical

13.Can you explain the difference between Type I and Type II errors?

Technical

14.Please define a Fourier transform and discuss how it is used?

General

15.Can you talk about deep learning and how it compares to other machine learning algorithms?

Technical

16.Please discuss the differences between generative and discriminative models?

Discovery

17.What are the most recent publications, papers or articles you have read about machine learning topics?

Discovery

18.What experience do you have performing research in the field of machine learning?

Discovery

19.Can you list some machine learning use cases which interest you?

Technical

Machine Learning Mock Interview

Please discuss the differences between generative and discriminative models?

20Machine Learning Interview Questions & Answers

1.In your opinion, what is the most valuable data applicable to our business?

2.What steps would you use to create and implement a data-based decision-making system for our company's users?

3.Can you briefly discuss the trade-offs between bias and variance?

4.Can you discuss the difference between supervised and unsupervised machine learning and when each one is used?

5.What is the difference between K-Nearest Neighbors and k-means clustering?

6.Can you talk about how precision and recall are used in the work you do?

7.Please discuss how a ROC curve works.

8.Can you define Bayes' Theorem and discuss how it is useful in the context of machine learning?

9.Bayes' Theorem is often described as 'Naive.' Why is Bayes naive?

10.Please discuss the purpose of regularization and explain the difference between L2 and L1 regularization.

11.Do you have a â€˜go-to' algorithm, and can you describe it to me?

12.What's the difference between the concepts of probability and likelihood?

13.Can you explain the difference between Type I and Type II errors?

14.Please define a Fourier transform and discuss how it is used?

15.Can you talk about deep learning and how it compares to other machine learning algorithms?

16.Please discuss the differences between generative and discriminative models?

17.What are the most recent publications, papers or articles you have read about machine learning topics?

18.What experience do you have performing research in the field of machine learning?

19.Can you list some machine learning use cases which interest you?

20.What is the purpose of pruning a decision tree?