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Proctor Level2_107

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See reviews Level2_107 MLP Level 1
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  1. 1
    What are the techniques for handling null values?
    Times asked 1
  2. 2
    How do you handle imbalanced datasets?
    Times asked 2
  3. 3
    Why did you try multiple models?
    Times asked 2
  4. 4
    What is the relationship between Information Gain and Entropy?
    Times asked 1
  5. 5
    Explain the Decision Tree building process.
    Times asked 2
  6. 6
    Why did you choose that learning rate for LightGBM?
    Times asked 1
  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.
    Times asked 1
  8. 8
    Name different activation functions.
    Times asked 2
  9. 9
    Explain the Tanh activation function (input and output).
    Times asked 1
See reviews Level2_107 MLP Level 1
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  1. 1
    What is the use of a correlation matrix?
    Times asked 1
  2. 2
    How do you handle imbalanced datasets?
    Times asked 2
  3. 3
    What techniques are used for handling null values?
    Times asked 1
  4. 4
    What is K-Fold Cross Validation?
    Times asked 2
  5. 5
    Which Cross Validation technique did you use?
    Times asked 1
  6. 6
    Did you drop any columns/features? Why?
    Times asked 1
  7. 7
    What is Precision?
    Times asked 2
  8. 8
    What is Recall?
    Times asked 2
  9. 9
    What is Support in a Classification Report?
    Times asked 2
  10. 10
    What is Macro Average in a Classification Report?
    Times asked 1
  11. 11
    What is Weighted Average in a Classification Report?
    Times asked 1
  12. 12
    What is the degree parameter in SVM?
    Times asked 1
  13. 13
    What is the C parameter in SVM?
    Times asked 1
  14. 14
    How does an SVM classifier work?
    Times asked 1
  15. 15
    What is an activation function?
    Times asked 1
  16. 16
    Why are activation functions used in Neural Networks?
    Times asked 1
  17. 17
    Name different activation functions.
    Times asked 2
  18. 18
    Explain the Tanh activation function.
    Times asked 1
  19. 19
    What is zero-centering?
    Times asked 1
  20. 20
    What are Neural Networks?
    Times asked 1
  21. 21
    Why did you try multiple models?
    Times asked 2
  22. 22
    What is Information Gain?
    Times asked 3
  23. 23
    How is Information Gain related to Entropy?
    Times asked 1
  24. 24
    Explain Entropy.
    Times asked 1
  25. 25
    How does a Decision Tree work?
    Times asked 2
  26. 26
    Explain the Decision Tree building process.
    Times asked 2
  27. 27
    Why did you choose a particular learning rate for LightGBM?
    Times asked 1
  28. 28
    Why did your score change after private evaluation?
    Times asked 1
  29. 29
    What EDA did you perform?
    Times asked 1
  30. 30
    What are the types of Machine Learning?
    Times asked 2
  31. 31
    Difference between Supervised and Unsupervised Learning.
    Times asked 2
  32. 32
    Difference between K-Means and K-Means++.
    Times asked 2
  33. 33
    How does clustering work?
    Times asked 1
  34. 34
    How would you use clustering on purchase/customer data?
    Times asked 1
  35. 35
    Are more features always better for training?
    Times asked 1
  36. 36
    What is the Curse of Dimensionality?
    Times asked 2
  37. 37
    How does PCA work?
    Times asked 3
  38. 38
    What is Gradient Descent?
    Times asked 2
  39. 39
    What is the Sigmoid function?
    Times asked 1
  40. 40
    Write the Sigmoid function formula.
    Times asked 2
  41. 41
    What threshold is used in Logistic Regression?
    Times asked 1
  42. 42
    Explain Logistic Regression.
    Times asked 5
  43. 43
    Can Logistic Regression handle outliers?
    Times asked 1
  44. 44
    What are the assumptions/conditions for Logistic Regression?
    Times asked 1
  45. 45
    What is Log Loss?
    Times asked 1
  46. 46
    What is the loss function of Linear Regression?
    Times asked 2
  47. 47
    Difference between RMSE and RMSLE.
    Times asked 1
  48. 48
    Why did you use RMSLE instead of RMSE?
    Times asked 1
  49. 49
    What is Naive Bayes?
    Times asked 1
  50. 50
    What is Machine Learning?
    Times asked 2
  51. 51
    Give examples of different Machine Learning algorithms.
    Times asked 1
  52. 52
    What models can be used for Sentiment Analysis?
    Times asked 1
  53. 53
    What is R² Score?
    Times asked 3
  54. 54
    How do you calculate mean and variance?
    Times asked 1
  55. 55
    How can you make a feature follow a normal distribution?
    Times asked 1
  56. 56
    Explain the T-Test.
    Times asked 1
  57. 57
    How do you compare the results obtained using RFE?
    Times asked 1
  58. 58
    Explain Bagging.
    Times asked 6
  59. 59
    Explain Boosting.
    Times asked 6
  60. 60
    What is the Bias-Variance Tradeoff?
    Times asked 3
  61. 61
    How does a Confusion Matrix work?
    Times asked 1
  62. 62
    How do you interpret a Confusion Matrix?
    Times asked 1
  63. 63
    What are feature selection techniques?
    Times asked 2
  64. 64
    Have you applied feature selection?
    Times asked 1
  65. 65
    How do you evaluate the performance of a feature selection technique?
    Times asked 1
  66. 66
    What is undersampling?
    Times asked 1
  67. 67
    What is oversampling?
    Times asked 1
  68. 68
    How do you identify whether a dataset is imbalanced?
    Times asked 1
  69. 69
    How do you choose the best model when multiple models have similar accuracy?
    Times asked 1
  70. 70
    How do you choose a model if some models take a very long time to train?
    Times asked 1
  71. 71
    How do you choose the learning rate (η) in Gradient Descent?
    Times asked 1
  72. 72
    What happens if the learning rate is too high or too low?
    Times asked 1
  73. 73
    Should the learning rate change during training?
    Times asked 1
  74. 74
    How are Precision, Recall, and F1-Score calculated?
    Times asked 1
  75. 75
    What are evaluation metrics for Classification?
    Times asked 1
  76. 76
    What are evaluation metrics for Regression?
    Times asked 1
  77. 77
    Coding: Build an SVM Pipeline (StandardScaler + SVM) on the MNIST/Digits dataset and generate the Classification Report.
    Times asked 1
  78. 78
    Coding: Compare SVM performance before and after applying PCA.
    Times asked 1
  79. 79
    Coding: Apply TF-IDF manually and calculate TF and IDF values for given documents.
    Times asked 1
  80. 80
    Coding: Calculate the mean and variance of given data points manually.
    Times asked 1
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