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1
What feature engineering techniques did you use?
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2
Which correlation method did you use?
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3
Why did you use StandardScaler (or your chosen scaler)?
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4
Difference between RandomSearchCV and GridSearchCV.
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5
How many combinations does RandomizedSearchCV try based on n_iter and cv?
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6
Why did you choose RandomizedSearchCV?
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7
What is the baseline model?
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8
If a node has 7 samples, will it split? Explain using min_samples_split and min_samples_leaf.
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9
If two features have 0 correlation, can they still be collinear?
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10
Explain Bagging vs Boosting.
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11
What is a Weak Learner?
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12
What preprocessing steps did you perform?
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13
Why didn't you use Accuracy as the evaluation metric?
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14
Formula of F1 Score.
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15
Does F1 Score give equal weight to Precision and Recall?
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16
Why did you use F1 Macro instead of other averaging methods?
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17
Which of your models are parametric and which are non-parametric?
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18
Why is Logistic Regression called "Regression" although it performs classification?
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19
Why did you use MLPClassifier?
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20
Explain your pipeline structure.
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21
Explain your hyperparameter tuning approach.
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22
Explain your model parameters.
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23
Why did you choose the models you used?
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24
How did you compare your models?
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25
What conclusions did you draw from model comparison?
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26
Explain your EDA graphs and the insights obtained.
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27
Explain the confidence interval/shaded region in a regression plot.
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28
Why is the correlation between OperationalHoursMeter and TargetValue nearly zero?
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29
What does errors="coerce" do while converting datetime?
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30
Did you use Pipelines or ColumnTransformer? Why?
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31
Did you use any user-defined functions?
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32
What is the output type after preprocessing (type(X_train))?
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33
What does fit_transform() return?
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34
How can you convert transformer output to a Pandas DataFrame (set_output(transform="pandas"))?
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35
What are the parameters of LightGBM?
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36
Explain Ridge and Lasso Regression.
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37
Which algorithms can perform feature selection?
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38
What does a Pair Plot show?
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39
Difference between Histogram and Bar Plot.
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40
What does Early Stopping mean?
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41
If the best score occurs at iteration 100, when does training stop with early stopping?
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42
What does TF-IDF do?
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43
How did you select the ensemble weights?
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44
Explain your train-test split and what changes if the split ratio changes.
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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.
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46
Load the Breast Cancer dataset and: Print target names. Count missing values.
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47
Load the California Housing dataset and fit HistGradientBoostingRegressor.
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48
Load the Wine dataset and print feature names.
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49
Load the Diabetes dataset and print the feature matrix.
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50
Load any toy dataset from scikit-learn and display its contents.
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51
Import a dummy dataset.
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52
Create a bar chart manually using model names and scores.
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53
Plot a graph comparing training scores of models.
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54
Plot the target value graph (including after log transformation if asked).
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55
Write simple Lasso Regression code.
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56
Explain your complete notebook.
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57
Explain EDA.
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58
Explain preprocessing.
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59
Explain feature engineering.
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60
Explain model selection.
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61
Explain hyperparameter tuning.
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62
Explain pipeline/ColumnTransformer.
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63
Explain model comparison.
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64
Explain graphs and visualizations.
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65
Explain assumptions and interpretations.
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66
Explain notebook improvements and limitations.