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Proctor Level2_106

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  1. 1
    Self introduction.
    Times asked 1
  2. 2
    Explain the problem statement.
    Times asked 6
  3. 3
    Explain your notebook to a non-technical person (e.g., your boss).
    Times asked 1
  4. 4
    Abstract questions about the dataset (not technical).
    Times asked 1
  5. 5
    Questions about college life.
    Times asked 1
  6. 6
    How many models did you try?
    Times asked 2
  7. 7
    How many models did you perform hyperparameter tuning on?
    Times asked 1
  8. 8
    Why didn't you submit earlier?
    Times asked 2
  9. 9
    Why F1 Score instead of Accuracy?
    Times asked 1
  10. 10
    How is F1 Score calculated?
    Times asked 1
  11. 11
    Macro vs Micro averaging.
    Times asked 1
  12. 12
    Why only Accuracy was considered?
    Times asked 1
  13. 13
    Did Hyperparameter Tuning improve your score?
    Times asked 1
  14. 14
    Which is your baseline model?
    Times asked 1
  15. 15
    What is imputation?
    Times asked 1
  16. 16
    Why did you choose this imputation strategy?
    Times asked 1
  17. 17
    What is scaling?
    Times asked 5
  18. 18
    What is normalization?
    Times asked 2
  19. 19
    What effect does normalization have on models?
    Times asked 1
  20. 20
    What do you think about outliers in your dataset?
    Times asked 1
  21. 21
    How did you handle class imbalance?
    Times asked 3
  22. 22
    Explain Hyperparameter Tuning.
    Times asked 1
  23. 23
    What did you do during Hyperparameter Tuning?
    Times asked 1
  24. 24
    Why didn't you perform Hyperparameter Tuning? (if missing from notebook)
    Times asked 1
  25. 25
    How much time did Hyperparameter Tuning take?
    Times asked 1
  26. 26
    Loss function of Logistic Regression.
    Times asked 2
  27. 27
    Loss function of Linear Regression.
    Times asked 1
  28. 28
    Overfitting vs Underfitting.
    Times asked 1
  29. 29
    How do you identify overfitting?
    Times asked 1
  30. 30
    How do you overcome overfitting?
    Times asked 1
  31. 31
    What is Cross Validation?
    Times asked 1
  32. 32
    Pipeline.
    Times asked 1
  33. 33
    PCA.
    Times asked 1
  34. 34
    Regularization.
    Times asked 1
  35. 35
    L1 vs L2 Penalty.
    Times asked 1
  36. 36
    Explain how L1/L2 Regularization works.
    Times asked 1
  37. 37
    Other ensemble models.
    Times asked 1
  38. 38
    Boosting vs XGBoost vs LightGBM.
    Times asked 1
  39. 39
    Explain how XGBoost works.
    Times asked 1
  40. 40
    Explain how LightGBM works.
    Times asked 1
  41. 41
    Can Gradient Boosting be converted into LightGBM?
    Times asked 1
  42. 42
    Which was your best model?
    Times asked 1
  43. 43
    Explain your best model in detail.
    Times asked 1
  44. 44
    Advantages of your best model.
    Times asked 1
  45. 45
    Difference between hstack() and vertical stacking (vstack()).
    Times asked 1
  46. 46
    Difference between Seaborn and Matplotlib.
    Times asked 1
  47. 47
    Load the Iris dataset.
    Times asked 1
  48. 48
    Train a Logistic Regression model on it.
    Times asked 1
  49. 49
    Self introduction
    Times asked 1
  50. 50
    Problem statement explanation
    Times asked 1
  51. 51
    Explain notebook to a non-technical person
    Times asked 1
  52. 52
    Abstract questions on dataset
    Times asked 1
  53. 53
    Questions about college life
    Times asked 1
  54. 54
    How many models tried?
    Times asked 1
  55. 55
    How many hyperparameter tuned?
    Times asked 1
  56. 56
    Why didn't you submit earlier?
    Times asked 2
  57. 57
    Why F1 instead of Accuracy?
    Times asked 1
  58. 58
    F1 Score calculation
    Times asked 1
  59. 59
    Macro vs Micro
    Times asked 1
  60. 60
    Imputation
    Times asked 1
  61. 61
    Imputation strategy
    Times asked 1
  62. 62
    Scaling
    Times asked 1
  63. 63
    Normalization and its effect
    Times asked 1
  64. 64
    Outliers
    Times asked 1
  65. 65
    Class imbalance handling
    Times asked 1
  66. 66
    Hyperparameter tuning explanation
    Times asked 1
  67. 67
    HPT improvement in score
    Times asked 1
  68. 68
    HPT execution time
    Times asked 1
  69. 69
    Logistic Regression loss function
    Times asked 1
  70. 70
    Linear Regression loss function
    Times asked 1
  71. 71
    Cross Validation
    Times asked 1
  72. 72
    PCA
    Times asked 1
  73. 73
    Pipeline
    Times asked 1
  74. 74
    Regularization
    Times asked 1
  75. 75
    L1 vs L2
    Times asked 1
  76. 76
    Overfitting vs Underfitting
    Times asked 1
  77. 77
    Detecting overfitting
    Times asked 1
  78. 78
    Preventing overfitting
    Times asked 1
  79. 79
    Boosting vs XGBoost vs LightGBM
    Times asked 1
  80. 80
    LightGBM working
    Times asked 1
  81. 81
    XGBoost working
    Times asked 1
  82. 82
    Other ensemble models
    Times asked 1
  83. 83
    Gradient Boosting → LightGBM conversion
    Times asked 1
  84. 84
    Best model explanation
    Times asked 1
  85. 85
    Advantages of best model
    Times asked 1
  86. 86
    hstack vs vstack
    Times asked 1
  87. 87
    Seaborn vs Matplotlib
    Times asked 1
  88. 88
    Logistic Regression coding on Iris dataset
    Times asked 1
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