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Lazy IITians Project Mentorship

Prepare in structure for the Project Mentorship.

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leve2_74 MLP Level 1 Aug 7, 2026
  1. 1
    Introduction.
  2. 2
    Explain the problem statement.
  3. 3
    Explain your notebook from start to finish.
  4. 4
    What is the learning rate?
  5. 5
    How does the learning rate affect overfitting and underfitting?
  6. 6
    Coding: Create a dummy Pipeline and add it to a ColumnTransformer.
  7. 7
    Coding: Use a different scaling method and a different imputation strategy.
  8. 8
    What is Data Preprocessing?
  9. 9
    What is EDA?
  10. 10
    What is Feature Engineering?
  11. 11
    What is Hyperparameter Tuning?
  12. 12
    What does random_state = 42 mean?
  13. 13
    Questions based on your notebook implementation.
Level3_46 MLP Level 1 Aug 7, 2026
  1. 1
    Explain your notebook/project.
  2. 2
    How did you handle outliers?
  3. 3
    Is removing outliers always a good practice?
  4. 4
    What can you do instead of removing outliers?
  5. 5
    What is collinearity?
  6. 6
    If two features are highly correlated, what should you do?
  7. 7
    Explain Bagging.
  8. 8
    Explain Boosting.
  9. 9
    How does Bagging work?
  10. 10
    How does Boosting work?
  11. 11
    Explain Decision Tree.
  12. 12
    How does a Decision Tree work?
  13. 13
    What is Entropy?
  14. 14
    Why are Bagging and Boosting better than a single Decision Tree?
  15. 15
    What are different evaluation metrics?
  16. 16
    Write the formulas for evaluation metrics.
  17. 17
    Why is Accuracy not always a good evaluation metric?
  18. 18
    For a diabetes dataset, which evaluation metric would you choose and why?
  19. 19
    How does Logistic Regression work?
  20. 20
    How does KNN work?
level3_36 MLP Level 1 Aug 7, 2026
  1. 1
    What is the problem statement?
  2. 2
    Explain your notebook/project.
  3. 3
    Explain Precision.
  4. 4
    Explain Recall.
  5. 5
    Explain F1-Score.
  6. 6
    Why did you use Label Encoding instead of other encoding techniques?
  7. 7
    Explain the hyperparameters you used.
  8. 8
    Explain Logistic Regression.
  9. 9
    Explain SVM.
  10. 10
    What is a kernel in SVM?
  11. 11
    Explain KNN.
  12. 12
    Why is KNN called a lazy learner?
  13. 13
    What are the different types of SVM kernels?
  14. 14
    Explain Bagging.
  15. 15
    Explain Boosting.
  16. 16
    Why did you use Random Forest?
  17. 17
    What are feature selection techniques?
  18. 18
    What is ROC-AUC?
  19. 19
    Why didn't you use ROC-AUC as the scoring metric?
level2_107_MLP_Viva MLP Level 1 Aug 7, 2026
  1. 1
    What are the techniques for handling null values?
  2. 2
    How do you handle imbalanced datasets?
  3. 3
    Why did you try multiple models?
  4. 4
    What is the relationship between Information Gain and Entropy?
  5. 5
    Explain the Decision Tree building process.
  6. 6
    Why did you choose that learning rate for LightGBM?
  7. 7
    Coding: On the MNIST dataset, create a Pipeline with StandardScaler and SVM, print the Classification Report, then apply PCA with SVM and compare the scores.
  8. 8
    Name different activation functions.
  9. 9
    Explain the Tanh activation function (input and output).
Leve1_110 MLP Level 1 Aug 7, 2026
  1. 1
    Level 2 Viva Advice
  2. 2
    Be extremely clear with your notebook.
  3. 3
    Be able to explain every command, parameter, and implementation you have used.
  4. 4
    Understand the logic, intuition, and mathematical concepts behind every step in your notebook.
level1_viva24 MLP Level 1 Aug 7, 2026
  1. 1
    Explain your notebook/project.
  2. 2
    How does Logistic Regression work?
  3. 3
    What is One-Hot Encoding? Give an example.
  4. 4
    Did you use Hyperparameter Tuning?
  5. 5
    Did you use Pipeline?
level1_118 MLP Level 1 Aug 7, 2026
  1. 1
    Show your ID card.
  2. 2
    Explain your notebook/project.
  3. 3
    Explain your notebook as a story.
  4. 4
    Explain your EDA.
  5. 5
    Explain the graphs you created.
  6. 6
    What insights did you draw from the graphs?
  7. 7
    Difference between a bar plot and a histogram.
  8. 8
    What does a pair plot show?
  9. 9
    What is the shaded region in a regression plot?
  10. 10
    Why is the correlation between two features zero?
  11. 11
    How does correlation affect model predictions?
  12. 12
    What does errors='coerce' do while converting dates?
  13. 13
    Explain your preprocessing steps.
  14. 14
    Explain your train-test split.
  15. 15
    What happens if the train-test split ratio changes?
  16. 16
    Explain your encoding.
  17. 17
    Explain your scaling.
  18. 18
    Explain your feature engineering.
  19. 19
    Why did you use ColumnTransformer?
  20. 20
    Why didn't you use Pipeline?
  21. 21
    Have you used user-defined functions?
  22. 22
    Explain your hyperparameter tuning.
  23. 23
    Why did you use RandomizedSearchCV?
  24. 24
    Difference between GridSearchCV and RandomizedSearchCV.
  25. 25
    How many times will RandomizedSearchCV run?
  26. 26
    What scoring metrics can be used in GridSearchCV?
  27. 27
    How do different scoring metrics affect model selection?
  28. 28
    What models did you use?
  29. 29
    Why did you choose those models?
  30. 30
    What is your baseline model?
  31. 31
    Why did you use only boosting models?
  32. 32
    Difference between Random Forest and XGBoost.
  33. 33
    What is Random Forest?
  34. 34
    Why didn't you choose Random Forest?
  35. 35
    What is XGBoost?
  36. 36
    What is LightGBM?
  37. 37
    What are the important LightGBM parameters?
  38. 38
    What is early_stopping?
  39. 39
    After early stopping, when is the best score reported?
  40. 40
    What are min_samples_split and min_samples_leaf?
  41. 41
    If a node has 7 samples, will it split?
  42. 42
    Which of your models are parametric?
  43. 43
    Which of your models are non-parametric?
  44. 44
    Why is Logistic Regression called "Regression" even though it performs classification?
  45. 45
    What is MLPClassifier?
  46. 46
    What is Ridge Regression?
  47. 47
    What is Lasso Regression?
  48. 48
    Which regression technique can be used for feature selection?
  49. 49
    What is Stacking?
  50. 50
    How does stacking work?
  51. 51
    How does a boosting model learn?
  52. 52
    How does a Ridge meta-model combine XGBoost, CatBoost, and LightGBM?
  53. 53
    How did you choose ensemble weights?
  54. 54
    Why did your ensemble use those weights?
  55. 55
    What is weak learner predictive power?
  56. 56
    How much data is used in Bagging?
  57. 57
    What is F1-Score?
  58. 58
    Write the formula for F1-Score.
  59. 59
    Does F1-Score give equal importance to Precision and Recall?
  60. 60
    Why did you use F1 Macro?
  61. 61
    Why didn't you use Accuracy?
  62. 62
    Explain the result comparison graph.
  63. 63
    Explain your model performance comparison.
  64. 64
    Coding: Load the Iris dataset and print the feature matrix.
  65. 65
    Coding: Load the Iris dataset and print the target values.
  66. 66
    Coding: Load the Iris dataset and separate features and target.
  67. 67
    Coding: Load the Diabetes dataset and print the feature matrix.
  68. 68
    Coding: Load the Breast Cancer dataset and print the target names.
  69. 69
    Coding: Load the California Housing dataset and train a HistGradientBoostingRegressor.
  70. 70
    Coding: Draw a histogram using the Diabetes dataset.
  71. 71
    Coding: Plot a bar graph comparing models and their scores.
  72. 72
    Coding: Plot the target variable after applying log transformation.
  73. 73
    Coding: Implement GridSearchCV or RandomizedSearchCV.
  74. 74
    Coding: Import a dummy/sample dataset.
  75. 75
    Coding: Implement Lasso Regression.
  76. 76
    Coding: Print the first few rows of the Iris dataset.
  77. 77
    Coding: Use fit_transform() output as a Pandas DataFrame using set_output(transform="pandas").
Level 1_116 MLP Level 1 Aug 7, 2026
  1. 1
    Introduction.
  2. 2
    Explain your notebook/project.
  3. 3
    Show and explain each section of your notebook.
  4. 4
    No additional theory questions were asked.
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