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Proctor mlp_level1_viva9

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  1. 1
    Show your ID card.
    Times asked 23
  2. 2
    Explain your notebook/project.
    Times asked 21
  3. 3
    Explain your EDA.
    Times asked 8
  4. 4
    Explain your univariate analysis.
    Times asked 1
  5. 5
    Explain your bivariate analysis.
    Times asked 1
  6. 6
    Explain the graphs you created and the insights from them.
    Times asked 1
  7. 7
    How did you handle missing values?
    Times asked 4
  8. 8
    What is SimpleImputer?
    Times asked 2
  9. 9
    How does SimpleImputer work?
    Times asked 1
  10. 10
    Explain your preprocessing pipeline.
    Times asked 1
  11. 11
    Have you used Pipeline?
    Times asked 2
  12. 12
    Why did you use (or not use) Pipeline?
    Times asked 1
  13. 13
    Explain your feature engineering.
    Times asked 6
  14. 14
    Did you create any new features?
    Times asked 1
  15. 15
    What encoding technique did you use?
    Times asked 1
  16. 16
    Why did you choose that encoder?
    Times asked 1
  17. 17
    Replace OrdinalEncoder with OneHotEncoder.
    Times asked 1
  18. 18
    Difference between Label Encoding and OneHot Encoding.
    Times asked 1
  19. 19
    What scaler did you use?
    Times asked 1
  20. 20
    Why did you choose MinMaxScaler over StandardScaler?
    Times asked 1
  21. 21
    Explain StandardScaler.
    Times asked 1
  22. 22
    Difference between StandardScaler and MinMaxScaler.
    Times asked 2
  23. 23
    What models did you try?
    Times asked 1
  24. 24
    Which model performed the best?
    Times asked 1
  25. 25
    Why didn't you explore linear models?
    Times asked 1
  26. 26
    Explain your model comparison.
    Times asked 2
  27. 27
    Explain the hyperparameters you tuned.
    Times asked 2
  28. 28
    What is the learning rate?
    Times asked 3
  29. 29
    Is a higher or lower learning rate better? Why?
    Times asked 1
  30. 30
    What is the Bias-Variance Tradeoff?
    Times asked 3
  31. 31
    What is Boosting?
    Times asked 2
  32. 32
    What is Gradient Boosting?
    Times asked 1
  33. 33
    What algorithm does Boosting use?
    Times asked 1
  34. 34
    What is Bagging?
    Times asked 2
  35. 35
    Difference between Bagging and Boosting.
    Times asked 4
  36. 36
    Difference between XGBoost and LightGBM.
    Times asked 5
  37. 37
    What is R² Score?
    Times asked 3
  38. 38
    Write the formula for R² Score.
    Times asked 1
  39. 39
    From which library do you import r2_score?
    Times asked 1
  40. 40
    From which library do you import OneHotEncoder?
    Times asked 1
  41. 41
    From which library do you import XGBoost?
    Times asked 1
  42. 42
    Why is Logistic Regression called "Regression" in a classification task?
    Times asked 1
  43. 43
    What is F1-Score?
    Times asked 3
  44. 44
    Write the formulas for Accuracy, Precision, and F1-Score.
    Times asked 1
  45. 45
    What are Ensemble methods?
    Times asked 1
  46. 46
    What resources did you use while building the project?
    Times asked 1
  47. 47
    Coding: Create a dataframe containing only categorical (object) columns.
    Times asked 1
  48. 48
    Coding: Apply OneHotEncoder to categorical columns with ≤10 unique values and find the new number of columns.
    Times asked 1
  49. 49
    Coding: Split the training data into numerical and categorical dataframes.
    Times asked 1
  50. 50
    Coding: Build separate preprocessing pipelines for numerical and categorical features using ColumnTransformer.
    Times asked 1
  51. 51
    Coding: Perform a train-test split.
    Times asked 1
  52. 52
    Coding: Print all numerical and non-numerical columns.
    Times asked 1
  53. 53
    Coding: Print Recall Score instead of Accuracy Score.
    Times asked 1
  54. 54
    Coding: Implement RandomForestRegressor (import, initialize, fit, predict).
    Times asked 1
  55. 55
    Coding: Build a simple preprocessing pipeline.
    Times asked 1
  56. 56
    Coding: Write a custom preprocessing pipeline.
    Times asked 1
  57. 57
    Coding: Apply Label Encoding to ["green", "blue", "white", "blue", "green"].
    Times asked 1
  58. 58
    Coding: Filter rows where RegionCode = "Florida".
    Times asked 1
  59. 59
    Coding: Filter rows where RegionCode = "Florida" and TargetValue > 50000.
    Times asked 1
  60. 60
    Coding: Create a subset of the data for RegionCode = "Florida".
    Times asked 1
  61. 61
    Coding: Drop two specified columns from the training dataset.
    Times asked 1
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