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Proctor Level2_103

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  1. 1
    Explain your Kaggle/Colab notebook (project).
    Times asked 1
  2. 2
    What steps did you take to improve your model/score?
    Times asked 1
  3. 3
    Why did you use this model?
    Times asked 1
  4. 4
    Why did you use this imputer?
    Times asked 1
  5. 5
    Why did you use this scaler?
    Times asked 1
  6. 6
    Explain the working of the models you used.
    Times asked 1
  7. 7
    Explain the parameters/hyperparameters used in your model.
    Times asked 1
  8. 8
    What is a decision tree?
    Times asked 1
  9. 9
    Compare K-Means Clustering and KNN.
    Times asked 1
  10. 10
    What is a loss function?
    Times asked 1
  11. 11
    Why do we need a loss function?
    Times asked 1
  12. 12
    What is feature selection?
    Times asked 1
  13. 13
    Why do we need feature selection?
    Times asked 1
  14. 14
    What is data preprocessing?
    Times asked 2
  15. 15
    What is EDA (Exploratory Data Analysis)?
    Times asked 1
  16. 16
    What is dimensionality reduction?
    Times asked 1
  17. 17
    Explain PCA.
    Times asked 1
  18. 18
    What is hyperparameter tuning?
    Times asked 2
  19. 19
    Explain Logistic Regression.
    Times asked 5
  20. 20
    What is overfitting?
    Times asked 5
  21. 21
    What is underfitting?
    Times asked 3
  22. 22
    How can you prevent overfitting and underfitting?
    Times asked 1
  23. 23
    What is GridSearchCV?
    Times asked 1
  24. 24
    What is RandomizedSearchCV?
    Times asked 1
  25. 25
    What is Cross Validation (CV)?
    Times asked 2
  26. 26
    What are evaluation metrics?
    Times asked 1
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