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1
What are the parameters of Logistic Regression?
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2
Can Logistic Regression be used to predict a continuous value (e.g., rainfall)? Why or why not?
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3
What are the parameters of Random Forest?
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4
What is the loss function of Random Forest?
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5
Difference between parameters and hyperparameters.
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6
What are the important XGBoost parameters?
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7
If XGBoost is overfitting, should you increase or decrease n_estimators? Why?
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8
If XGBoost is overfitting, should you increase or decrease learning_rate? Why?
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9
What does colsample_bytree / colsample_bylevel / colsample_bynode (referred to as col_by_sample) do?
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10
What is n_iter in RandomizedSearchCV?
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11
What does verbose mean?
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12
What does n_jobs do?
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13
What is the mathematical range of R² score?
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14
Why can R² be negative?
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15
What is data leakage?
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16
How did you ensure data leakage was avoided?
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17
Why should you not train the final model on the entire dataset before evaluation?
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18
How do you ensure reproducibility of results?
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19
What is autocorrelation?
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20
Explain the parameters of TF-IDF.
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21
Create a preprocessing Pipeline using: SimpleImputer RobustScaler KNNImputer OneHotEncoder
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22
Add RobustScaler to an existing preprocessing pipeline.
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23
Explain what changes after adding RobustScaler.
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24
Load the Diabetes dataset.
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25
Convert it into a Pandas DataFrame.
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26
Print the number of features.
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27
Fit a Logistic Regression model on it.
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28
Explain EDA overall, not line by line.
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29
Mention one important observation from the dataset.
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30
Show all model score comparisons.
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31
How many models were trained and how were they compared?
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32
Explain the mandatory Level-1 notebook sections one by one.
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33
Were any custom/user-defined functions used?
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34
Were any LLMs used while building the notebook?
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35
Parameters of Logistic Regression
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36
Parameters of Random Forest
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37
Loss function of Random Forest
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38
Difference between Parameters and Hyperparameters
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39
Important XGBoost parameters
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40
Effect of n_estimators on overfitting
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41
Effect of learning_rate on overfitting
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42
colsample_* parameter in XGBoost
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43
verbose
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44
n_jobs
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45
Mathematical range of R²
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46
Why R² can be negative
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47
Data Leakage
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48
How to prevent Data Leakage
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49
Why not train the final model on the full dataset before evaluation
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50
Reproducibility of results
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51
Autocorrelation
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52
TF-IDF parameters
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53
Can Logistic Regression predict rainfall? Why/Why not?
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54
Create/Edit preprocessing pipeline with RobustScaler, KNNImputer, OHE
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55
Load Diabetes dataset → DataFrame → Number of features → Fit Logistic Regression
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56
Explain one key insight from EDA
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57
Compare all trained models
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58
User-defined functions
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59
LLM usage in the notebook