Here are the top interview questions that one should look before going for an Machine Learning or Data Science or Data Analyst interview. These questions has been asked in almost every interview. Ensure that you know these to give a sure shot to crack your interviews. Here we have the first set of 25 questions you can glance and more will be in the next posts.
ML Interview Questions
| 1. Explain AUC curve |
| 2. Difference between Supervised and Un-Supervised Learning |
| 3. What’s the trade-off between bias and variance? |
| 4. How is KNN different from k-means clustering? |
| 5. Explain how a ROC curve works. |
| 6. Define precision and recall. |
| 7. Explain Bayes Theorem and its formula |
| 8. Why Naïve Bayes called as Naïve? |
| 9. What is linear regression? What do the terms r-squared and p-value mean? What is the significance of each of these components? |
| 10. What are the assumptions required for linear regression? |
| 11. What are the drawbacks of linear regression? |
| 12. How does multi-collinearity affect the linear regression? |
| 13. How do you identify the significance of an independent variable in linear regression? |
| 14. Explain how outliers impact linear regression? |
| 15. What is the role of High leverage points in linear regression? |
| 16. Explain how do you identify the non-linearity in the data ? Also, explain what is the affect of non-linear data on linear regression? |
| 17. What are Residual plots? |
| 18. What is Heteroscedasticity? |
| 19. What is Homoscedasticity? |
| 20. How do you test correlation between error terms in linear regression? |
| 21. How do you check if your model is significant or not? |
| 22. What are requirements for building a linear regression? |
| 23. Explain Ridge Regression? |
| 24. Explain the difference between L1 and L2 regularization. |
| 25. Which is more important to you: model accuracy or model performance? |
Leave a Reply