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Learn Theoretical Questions | Scikit-learn
Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
2. NumPy
3. Pandas
4. Matplotlib
5. Seaborn
6. Statistics
7. Scikit-learn

book
Theoretical Questions

1. How do you handle overfitting in a model?

2. Explain bias-variance trade-off.

3. What is early stopping in the context of training a model?

4. How would you handle imbalanced datasets?

5. Which of the following best describes the difference between data normalization and scaling?

6. How does cross-validation work?

7. Which statement best describes the difference between precision and recall?

8. Which kind of models are utilized by the bagging ensemble method?

9. How does a Random Forest algorithm function?

10. Which of the following is not an ensemble method?

11. In which scenario is a high recall more important than high precision?

question mark

How do you handle overfitting in a model?

Select a few correct answers

question mark

Explain bias-variance trade-off.

Select the correct answer

question mark

What is early stopping in the context of training a model?

Select the correct answer

question mark

How would you handle imbalanced datasets?

Select a few correct answers

question mark

Which of the following best describes the difference between data normalization and scaling?

Select the correct answer

question mark

How does cross-validation work?

Select the correct answer

question mark

Which statement best describes the difference between precision and recall?

Select the correct answer

question mark

Which kind of models are utilized by the bagging ensemble method?

Select the correct answer

question mark

How does a Random Forest algorithm function?

Select the correct answer

question mark

Which of the following is not an ensemble method?

Select the correct answer

question mark

In which scenario is a high recall more important than high precision?

Select the correct answer

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How can we improve it?

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Section 7. Chapter 6
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