20 data science interview questions
1. Explain Logistic Regression and its assumptions.
2. Explain Linear Regression and its assumptions
3. How do you split your data between training and validation?
4. Describe Binary Classification.
5. Explain the working of decision trees.
6. What are different metrics to classify a dataset?
7. What’s the role of a cost function?
8. What’s the difference between convex and non-convex cost function?
9. Why is it important to know bias-variance trade off while modeling?
10. Why is regularization used in machine learning models? What are the differences between L1 and L2 regularization?
11. What’s the problem of exploding gradients in machine learning?
12. Is it necessary to use activation functions in neural networks?
13. In what aspects is a box plot different from a histogram?
14. What is cross validation? Why is it used?
15. Can you explain the concept of false positive and false negative?
16. Explain how SVM works.
17. While working at Facebook, you’re asked to implement some new features. What type of experiment would you run to implement these features?
18. What techniques can be used to evaluate a Machine Learning model?
19. Why is overfitting a problem in machine learning models? What steps can you take to avoid it?
20. Describe a way to detect anomalies in a given dataset.
21. What are the Naive Bayes fundamentals?
22. What is AUC – ROC Curve?
23. What is K-means?
24. How does the Gradient Boosting algorithm work?
25. Explain advantages and drawbacks of Support Vector Machines (SVM).
26. What is the difference between bagging and boosting?
27. Before building any model, why do we need the feature selection/engineering step?
28. How to deal with unbalanced binary classification?
20 data science interview questions
