Question 1

How do you handle missing or corrupted data in a dataset?

Accepted Answer

A) Drop missing rows or columns 
B) Replace missing values with mean/median/mode 
C) Assign a unique category to missing values 
D) All of the above

Question 2

When performing regression or classification, which of the following is the correct way to preprocess the data?

Accepted Answer

A) Normalize the data -> PCA -> training 
B) PCA -> normalize PCA output -> training 
C) Normalize the data -> PCA -> normalize PCA output -> training 
D) None of the above

Question 3

The model will be trained with data in one single batch is known as ?

Accepted Answer

A) Batch learning 
B) Offline learning 
C) Both A and B 
D) None of the above

Question 4

Which of the following is a categorical outcome?

Accepted Answer

A) RMSE 
B) RSquared 
C) Accuracy 
D) All of the Mentioned

Question 5

Which of the following statements about regularization is not correct?

Accepted Answer

A) Using too large a value of lambda can cause your hypothesis to underfit the data. 
B) Using too large a value of lambda can cause your hypothesis to overfit the data 
C) Using a very large value of lambda cannot hurt the performance of your hypothesis. 
D) None of the above

Exam 3: Machine Learning and Data Analysis

How do you handle missing or corrupted data in a dataset?

When performing regression or classification, which of the following is the correct way to preprocess the data?

The model will be trained with data in one single batch is known as ?

Which of the following is a categorical outcome?

Which of the following statements about regularization is not correct?