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Proctor Level3_30

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  1. 1
    Introduction.
    Times asked 9
  2. 2
    Explain your notebook/project.
    Times asked 21
  3. 3
    How did you handle missing (NaN) values?
    Times asked 1
  4. 4
    Which imputer should be used when a feature contains outliers?
    Times asked 1
  5. 5
    How does Logistic Regression work?
    Times asked 3
  6. 6
    What is the loss function of Logistic Regression?
    Times asked 3
  7. 7
    How does Linear Regression work?
    Times asked 1
  8. 8
    What is the loss function of Linear Regression?
    Times asked 2
  9. 9
    How does Random Forest work?
    Times asked 1
  10. 10
    How does XGBoost work?
    Times asked 2
  11. 11
    Difference between Bagging and Boosting.
    Times asked 4
  12. 12
    How can you handle an imbalanced dataset?
    Times asked 1
  13. 13
    What is the Pearson Correlation formula?
    Times asked 1
  14. 14
    Do all trees in a Random Forest receive all features?
    Times asked 1
  15. 15
    What are the types of feature reduction techniques?
    Times asked 1
  16. 16
    How does PCA work?
    Times asked 3
  17. 17
    Is PCA linear or non-linear?
    Times asked 1
  18. 18
    What are Eigenvectors?
    Times asked 1
  19. 19
    How do you calculate PCA manually?
    Times asked 1
  20. 20
    How do you calculate Eigenvectors manually?
    Times asked 1
  21. 21
    Why does L1 Regularization eliminate features?
    Times asked 1
  22. 22
    Why does L2 Regularization help prevent overfitting?
    Times asked 1
  23. 23
    In a poisonous apple detection problem, would you prioritize Precision or Recall? Why?
    Times asked 1
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