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Machine Learning Basics
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Machine Learning Basics
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25 Questions

1. What is SelectPercentile?

2. What are two issues with overfitting and underfitting?

3. What is the ideal feature selection?

4. What problems are great for unsupervised learning?

5. What is least squares in linear regression?

6. What is a neural network?

7. What is naive bayes?

8. What is gradient descent?

9. How does K-means optimize itself?

10. What are some challenges with K-means clustering?

11. What is SelectKBest?

12. What are the two steps to K-means clustering?

13. What two sets are used to train the classifier

14. What is a support vector machine?

15. What is feature scaling?

16. What range is used for feature scaling

17. What is the log loss function?

18. What is k-means clustering?

19. What is high bias?

20. What is high variance?

21. What does Lasso Regression do?

22. What is the kernal method?

23. What two algorithms does features scaling help with?

24. What is regularization?

25. What is a good clustering algorithm?

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