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Proctor mlp_level1_viva14

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  1. 1
    Introduction.
    Times asked 9
  2. 2
    Show your ID card.
    Times asked 23
  3. 3
    Explain your notebook/project.
    Times asked 21
  4. 4
    Explain your notebook as a story.
    Times asked 2
  5. 5
    Explain your project flow from start to finish.
    Times asked 1
  6. 6
    Explain the machine learning models you used.
    Times asked 1
  7. 7
    Explain the preprocessing pipeline you used.
    Times asked 1
  8. 8
    Explain the hyperparameter tuning process.
    Times asked 1
  9. 9
    What is the cv parameter in GridSearchCV/RandomizedSearchCV?
    Times asked 1
  10. 10
    How does Cross Validation work?
    Times asked 2
  11. 11
    What is K-Fold Cross Validation?
    Times asked 2
  12. 12
    Why is a validation set needed?
    Times asked 1
  13. 13
    How many iterations/model fits will be performed based on your hyperparameter tuning?
    Times asked 1
  14. 14
    Difference between GridSearchCV and RandomizedSearchCV.
    Times asked 5
  15. 15
    How do you identify whether a model is overfitting or underfitting?
    Times asked 1
  16. 16
    How do you reduce overfitting?
    Times asked 2
  17. 17
    What happens if the correlation between features is high?
    Times asked 1
  18. 18
    How many models did you try?
    Times asked 2
  19. 19
    Did you try different preprocessing techniques?
    Times asked 1
  20. 20
    Did you try different models?
    Times asked 1
  21. 21
    What resources did you use?
    Times asked 2
  22. 22
    What other approaches did you try?
    Times asked 1
  23. 23
    What difficulties did you face while doing the project?
    Times asked 1
  24. 24
    Have you used any sklearn Pipelines?
    Times asked 1
  25. 25
    Explain your missing value handling.
    Times asked 1
  26. 26
    How did you handle numerical missing values?
    Times asked 2
  27. 27
    How did you handle class imbalance?
    Times asked 3
  28. 28
    Why did you use One-Hot Encoding?
    Times asked 1
  29. 29
    Have you used scaling?
    Times asked 1
  30. 30
    Why did you use StandardScaler?
    Times asked 2
  31. 31
    How does StandardScaler work?
    Times asked 2
  32. 32
    Difference between StandardScaler and other scaling techniques.
    Times asked 1
  33. 33
    What is scaling?
    Times asked 5
  34. 34
    Why didn't you use scaling?
    Times asked 2
  35. 35
    What is the learning rate parameter?
    Times asked 1
  36. 36
    Which scoring metric did you use to compare models?
    Times asked 1
  37. 37
    Why did you use F1-Score, Precision, and Recall instead of RMSE?
    Times asked 1
  38. 38
    What happens if RMSE is used for Logistic Regression?
    Times asked 1
  39. 39
    Explain the ROC Curve.
    Times asked 1
  40. 40
    Explain the Confusion Matrix.
    Times asked 1
  41. 41
    Point out True Positive (TP) and True Negative (TN) in the Confusion Matrix.
    Times asked 1
  42. 42
    What is log1p?
    Times asked 1
  43. 43
    What is the mean and standard deviation after StandardScaler?
    Times asked 1
  44. 44
    Coding: Implement RandomizedSearchCV for Logistic Regression.
    Times asked 1
  45. 45
    Coding: Train an ElasticNet model and evaluate its performance.
    Times asked 1
  46. 46
    Coding: Train a Linear Regression model on the raw (non-preprocessed) dataset.
    Times asked 1
  47. 47
    Coding: Load the training dataset and print the first 6/7/10 rows.
    Times asked 1
  48. 48
    Coding: Filter rows where ManufactureYear is greater than a given year.
    Times asked 1
  49. 49
    Coding: Print unique/value counts of VendorPartnerID.
    Times asked 1
  50. 50
    Coding: Find the proportion of each category (cardinality) in a feature.
    Times asked 1
  51. 51
    Coding: Load the Iris dataset and train a Ridge Regression model.
    Times asked 1
  52. 52
    Coding: Load the California Housing dataset.
    Times asked 2
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