Question 1

Given a DataFrame df:

df = pd.DataFrame({

'A': [1, 2, 3],

'B': [4, 5, 6]

})

You want to square each individual element in the DataFrame. Which of the following code will accomplish this?

Options :

A : df.applymap(lambda x: x**2)

B : df['A'].apply(lambda x: x**2)

C : df.map(lambda x: x**2)

D : df.apply(lambda x: x**2, axis=1)

Answer: A

Question 2

When dealing with a categorical feature that has high cardinality in a dataset, which of the following approaches is most advisable for one-hot encoding?

Options :

A : Use label encoding instead of one-hot encoding

B : Group less frequent categories into a single "Other" category before one-hot encoding

C : Remove the feature from the dataset

D : One-hot encode all unique values in the feature

Answer: B

Question 3

Suppose you are working on a classification problem with an imbalanced dataset. Which of the following techniques would not be effective for addressing the issue?

Options :

A : Using accuracy as the evaluation metric

B : Using the Synthetic Minority Over-sampling Technique (SMOTE)

C : Using different evaluation metrics such as F1-score.

D : Under-sampling the majority class

Answer: A

Question 4

Which of the following cross-validation techniques is most appropriate when you are working with a dataset where the target variable's classes are imbalanced?

Options :

A : Leave-One-Out Cross-Validation (LOOCV)

B : k-Fold Cross-Validation

C : Stratified k-Fold Cross-Validation

D : Random Split Cross-Validation

Answer: C

Question 5

While evaluating a logistic regression model using a confusion matrix, you notice that the True Positive rate is significantly higher than the True Negative rate. What does this observation imply?

Options :

A : The model is better at identifying the positive class than the negative class

B : The model performs equally well for both classes

C : The model has a high level of bias

D : The model is overfitting

Answer: A

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