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Вивчайте Theoretical Questions | Scikit-learn
Data Science Interview Challenge
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Зміст курсу

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?

Виберіть кілька правильних відповідей

question mark

Explain bias-variance trade-off.

Виберіть правильну відповідь

question mark

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

Виберіть правильну відповідь

question mark

How would you handle imbalanced datasets?

Виберіть кілька правильних відповідей

question mark

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

Виберіть правильну відповідь

question mark

How does cross-validation work?

Виберіть правильну відповідь

question mark

Which statement best describes the difference between precision and recall?

Виберіть правильну відповідь

question mark

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

Виберіть правильну відповідь

question mark

How does a Random Forest algorithm function?

Виберіть правильну відповідь

question mark

Which of the following is not an ensemble method?

Виберіть правильну відповідь

question mark

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

Виберіть правильну відповідь

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