Machine Learning Mock Interview

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

Question 7 of 20

Please discuss how a ROC curve works.

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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.

This technical question assumes that you know the definition of the concept the interviewer is questioning you about, and you can describe how it functions. As with any technical question, your answer should begin with a brief definition of the term, followed by a description of how it is used in work done by a machine learning engineer. You may or may not want to include an example. You should also anticipate follow-up questions, which indicate that the interviewer has a strong interest in this topic or that it is important to their work.

William Swansen

"A ROC curve provides a graphical picture of the difference between the true positive rates and the false positive rate at various thresholds. This helps the user to better understand the differences between the sensitivity of the model, represented by the true positives, and the probability of the model providing inaccurate information, also known as false positives."

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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 how a ROC curve works.

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?