1. What are feature vectors?
2. What are the Steps to Create a Decision Tree?
3. Describe Root Cause Analysis?
4. What does Logistic Regression mean?
5. What does Recommender Systems Denote?
6. Explain What is Cross-Validation in Detail?
7. What does Collaborative Filtering Stand for?
8. Are Gradient Descent Methods Designed to Converge at the Similar Point Every Time?
9. What is the Ultimate Purpose of A/B Testing?
10. What are the Disadvantages of Using the Linear Model?
11. What is the definition of Law of Large Numbers?
12. What does Confounding Variables Refer to?
13. Give an explanation about Star Schema
14. How Frequently Should an Algorithm be Updated?
15. What do Eigenvalue and Eigenvector Denote?
16. What are the Different Types of Biases that you may Witness During Sampling?
17. What is Selective Bias?
18. What does Survivorship Bias Stand for?
19. Do you Know How Resampling is Done?
20. Working for a Random Forest?
Resource:- 20 Most Popular Data Science Interview Questions & Answers
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