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