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1
Introduction.
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2
Explain your notebook/project.
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3
How did you handle missing (NaN) values?
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4
Which imputer should be used when a feature contains outliers?
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5
How does Logistic Regression work?
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6
What is the loss function of Logistic Regression?
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7
How does Linear Regression work?
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8
What is the loss function of Linear Regression?
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9
How does Random Forest work?
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10
How does XGBoost work?
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11
Difference between Bagging and Boosting.
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12
How can you handle an imbalanced dataset?
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13
What is the Pearson Correlation formula?
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14
Do all trees in a Random Forest receive all features?
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15
What are the types of feature reduction techniques?
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16
How does PCA work?
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17
Is PCA linear or non-linear?
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18
What are Eigenvectors?
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19
How do you calculate PCA manually?
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20
How do you calculate Eigenvectors manually?
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21
Why does L1 Regularization eliminate features?
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22
Why does L2 Regularization help prevent overfitting?
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23
In a poisonous apple detection problem, would you prioritize Precision or Recall? Why?