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Proctor Level1_118

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143 Questions
2 Sets
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See reviews Level1_118 MLP Level 1
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  1. 1
    Show your ID card.
    Times asked 22
  2. 2
    Explain your notebook/project.
    Times asked 21
  3. 3
    Explain your notebook as a story.
    Times asked 2
  4. 4
    Explain your EDA.
    Times asked 8
  5. 5
    Explain the graphs you created.
    Times asked 1
  6. 6
    What insights did you draw from the graphs?
    Times asked 1
  7. 7
    Difference between a bar plot and a histogram.
    Times asked 1
  8. 8
    What does a pair plot show?
    Times asked 1
  9. 9
    What is the shaded region in a regression plot?
    Times asked 1
  10. 10
    Why is the correlation between two features zero?
    Times asked 1
  11. 11
    How does correlation affect model predictions?
    Times asked 1
  12. 12
    What does errors='coerce' do while converting dates?
    Times asked 1
  13. 13
    Explain your preprocessing steps.
    Times asked 2
  14. 14
    Explain your train-test split.
    Times asked 1
  15. 15
    What happens if the train-test split ratio changes?
    Times asked 1
  16. 16
    Explain your encoding.
    Times asked 2
  17. 17
    Explain your scaling.
    Times asked 2
  18. 18
    Explain your feature engineering.
    Times asked 6
  19. 19
    Why did you use ColumnTransformer?
    Times asked 1
  20. 20
    Why didn't you use Pipeline?
    Times asked 1
  21. 21
    Have you used user-defined functions?
    Times asked 1
  22. 22
    Explain your hyperparameter tuning.
    Times asked 3
  23. 23
    Why did you use RandomizedSearchCV?
    Times asked 1
  24. 24
    Difference between GridSearchCV and RandomizedSearchCV.
    Times asked 5
  25. 25
    How many times will RandomizedSearchCV run?
    Times asked 1
  26. 26
    What scoring metrics can be used in GridSearchCV?
    Times asked 1
  27. 27
    How do different scoring metrics affect model selection?
    Times asked 1
  28. 28
    What models did you use?
    Times asked 3
  29. 29
    Why did you choose those models?
    Times asked 3
  30. 30
    What is your baseline model?
    Times asked 1
  31. 31
    Why did you use only boosting models?
    Times asked 1
  32. 32
    Difference between Random Forest and XGBoost.
    Times asked 2
  33. 33
    What is Random Forest?
    Times asked 1
  34. 34
    Why didn't you choose Random Forest?
    Times asked 1
  35. 35
    What is XGBoost?
    Times asked 2
  36. 36
    What is LightGBM?
    Times asked 1
  37. 37
    What are the important LightGBM parameters?
    Times asked 1
  38. 38
    What is early_stopping?
    Times asked 1
  39. 39
    After early stopping, when is the best score reported?
    Times asked 1
  40. 40
    What are min_samples_split and min_samples_leaf?
    Times asked 1
  41. 41
    If a node has 7 samples, will it split?
    Times asked 1
  42. 42
    Which of your models are parametric?
    Times asked 1
  43. 43
    Which of your models are non-parametric?
    Times asked 1
  44. 44
    Why is Logistic Regression called "Regression" even though it performs classification?
    Times asked 1
  45. 45
    What is MLPClassifier?
    Times asked 1
  46. 46
    What is Ridge Regression?
    Times asked 1
  47. 47
    What is Lasso Regression?
    Times asked 1
  48. 48
    Which regression technique can be used for feature selection?
    Times asked 1
  49. 49
    What is Stacking?
    Times asked 1
  50. 50
    How does stacking work?
    Times asked 1
  51. 51
    How does a boosting model learn?
    Times asked 1
  52. 52
    How does a Ridge meta-model combine XGBoost, CatBoost, and LightGBM?
    Times asked 1
  53. 53
    How did you choose ensemble weights?
    Times asked 1
  54. 54
    Why did your ensemble use those weights?
    Times asked 1
  55. 55
    What is weak learner predictive power?
    Times asked 1
  56. 56
    How much data is used in Bagging?
    Times asked 1
  57. 57
    What is F1-Score?
    Times asked 3
  58. 58
    Write the formula for F1-Score.
    Times asked 2
  59. 59
    Does F1-Score give equal importance to Precision and Recall?
    Times asked 1
  60. 60
    Why did you use F1 Macro?
    Times asked 1
  61. 61
    Why didn't you use Accuracy?
    Times asked 1
  62. 62
    Explain the result comparison graph.
    Times asked 1
  63. 63
    Explain your model performance comparison.
    Times asked 1
  64. 64
    Coding: Load the Iris dataset and print the feature matrix.
    Times asked 1
  65. 65
    Coding: Load the Iris dataset and print the target values.
    Times asked 1
  66. 66
    Coding: Load the Iris dataset and separate features and target.
    Times asked 1
  67. 67
    Coding: Load the Diabetes dataset and print the feature matrix.
    Times asked 1
  68. 68
    Coding: Load the Breast Cancer dataset and print the target names.
    Times asked 1
  69. 69
    Coding: Load the California Housing dataset and train a HistGradientBoostingRegressor.
    Times asked 1
  70. 70
    Coding: Draw a histogram using the Diabetes dataset.
    Times asked 1
  71. 71
    Coding: Plot a bar graph comparing models and their scores.
    Times asked 1
  72. 72
    Coding: Plot the target variable after applying log transformation.
    Times asked 1
  73. 73
    Coding: Implement GridSearchCV or RandomizedSearchCV.
    Times asked 1
  74. 74
    Coding: Import a dummy/sample dataset.
    Times asked 1
  75. 75
    Coding: Implement Lasso Regression.
    Times asked 1
  76. 76
    Coding: Print the first few rows of the Iris dataset.
    Times asked 1
  77. 77
    Coding: Use fit_transform() output as a Pandas DataFrame using set_output(transform="pandas").
    Times asked 1
See reviews Level1_118 MLP Level 1
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  1. 1
    What feature engineering techniques did you use?
    Times asked 1
  2. 2
    Which correlation method did you use?
    Times asked 2
  3. 3
    Why did you use StandardScaler (or your chosen scaler)?
    Times asked 1
  4. 4
    Difference between RandomSearchCV and GridSearchCV.
    Times asked 1
  5. 5
    How many combinations does RandomizedSearchCV try based on n_iter and cv?
    Times asked 1
  6. 6
    Why did you choose RandomizedSearchCV?
    Times asked 1
  7. 7
    What is the baseline model?
    Times asked 1
  8. 8
    If a node has 7 samples, will it split? Explain using min_samples_split and min_samples_leaf.
    Times asked 1
  9. 9
    If two features have 0 correlation, can they still be collinear?
    Times asked 1
  10. 10
    Explain Bagging vs Boosting.
    Times asked 1
  11. 11
    What is a Weak Learner?
    Times asked 1
  12. 12
    What preprocessing steps did you perform?
    Times asked 2
  13. 13
    Why didn't you use Accuracy as the evaluation metric?
    Times asked 1
  14. 14
    Formula of F1 Score.
    Times asked 1
  15. 15
    Does F1 Score give equal weight to Precision and Recall?
    Times asked 1
  16. 16
    Why did you use F1 Macro instead of other averaging methods?
    Times asked 1
  17. 17
    Which of your models are parametric and which are non-parametric?
    Times asked 1
  18. 18
    Why is Logistic Regression called "Regression" although it performs classification?
    Times asked 1
  19. 19
    Why did you use MLPClassifier?
    Times asked 1
  20. 20
    Explain your pipeline structure.
    Times asked 1
  21. 21
    Explain your hyperparameter tuning approach.
    Times asked 1
  22. 22
    Explain your model parameters.
    Times asked 1
  23. 23
    Why did you choose the models you used?
    Times asked 1
  24. 24
    How did you compare your models?
    Times asked 1
  25. 25
    What conclusions did you draw from model comparison?
    Times asked 1
  26. 26
    Explain your EDA graphs and the insights obtained.
    Times asked 1
  27. 27
    Explain the confidence interval/shaded region in a regression plot.
    Times asked 1
  28. 28
    Why is the correlation between OperationalHoursMeter and TargetValue nearly zero?
    Times asked 1
  29. 29
    What does errors="coerce" do while converting datetime?
    Times asked 1
  30. 30
    Did you use Pipelines or ColumnTransformer? Why?
    Times asked 1
  31. 31
    Did you use any user-defined functions?
    Times asked 1
  32. 32
    What is the output type after preprocessing (type(X_train))?
    Times asked 1
  33. 33
    What does fit_transform() return?
    Times asked 1
  34. 34
    How can you convert transformer output to a Pandas DataFrame (set_output(transform="pandas"))?
    Times asked 1
  35. 35
    What are the parameters of LightGBM?
    Times asked 1
  36. 36
    Explain Ridge and Lasso Regression.
    Times asked 1
  37. 37
    Which algorithms can perform feature selection?
    Times asked 1
  38. 38
    What does a Pair Plot show?
    Times asked 1
  39. 39
    Difference between Histogram and Bar Plot.
    Times asked 1
  40. 40
    What does Early Stopping mean?
    Times asked 1
  41. 41
    If the best score occurs at iteration 100, when does training stop with early stopping?
    Times asked 1
  42. 42
    What does TF-IDF do?
    Times asked 1
  43. 43
    How did you select the ensemble weights?
    Times asked 1
  44. 44
    Explain your train-test split and what changes if the split ratio changes.
    Times asked 1
  45. 45
    Load the Iris dataset and: Display the feature matrix. Separate features and target. Print feature matrix shape. Print target variable. Print feature names. Print data without the target column.
    Times asked 1
  46. 46
    Load the Breast Cancer dataset and: Print target names. Count missing values.
    Times asked 1
  47. 47
    Load the California Housing dataset and fit HistGradientBoostingRegressor.
    Times asked 1
  48. 48
    Load the Wine dataset and print feature names.
    Times asked 1
  49. 49
    Load the Diabetes dataset and print the feature matrix.
    Times asked 1
  50. 50
    Load any toy dataset from scikit-learn and display its contents.
    Times asked 1
  51. 51
    Import a dummy dataset.
    Times asked 1
  52. 52
    Create a bar chart manually using model names and scores.
    Times asked 1
  53. 53
    Plot a graph comparing training scores of models.
    Times asked 1
  54. 54
    Plot the target value graph (including after log transformation if asked).
    Times asked 1
  55. 55
    Write simple Lasso Regression code.
    Times asked 1
  56. 56
    Explain your complete notebook.
    Times asked 1
  57. 57
    Explain EDA.
    Times asked 1
  58. 58
    Explain preprocessing.
    Times asked 1
  59. 59
    Explain feature engineering.
    Times asked 1
  60. 60
    Explain model selection.
    Times asked 1
  61. 61
    Explain hyperparameter tuning.
    Times asked 1
  62. 62
    Explain pipeline/ColumnTransformer.
    Times asked 1
  63. 63
    Explain model comparison.
    Times asked 1
  64. 64
    Explain graphs and visualizations.
    Times asked 1
  65. 65
    Explain assumptions and interpretations.
    Times asked 1
  66. 66
    Explain notebook improvements and limitations.
    Times asked 1
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