1. The following procedure finds the highest marks in Mathematics scored by a “male” student and a
“female” student from the “Scores”
dataset. But the programmer may have made mistakes in one or
more steps. Identify all such steps (if any). It is a Multiple Select Question (MSQ).
Step 1. Arrange all cards in a single pile called Pile 1
Step 2. Maintain two variables Mmax, Fmax and initialize them to 0
Step 3. If Pile 1 is empty then stop the iteration
Step 4. Read the top card in Pile 1
Step 5. If Gender is “M” and Mmax > Mathematics marks then store Mathematics marks in Mmax
Step 6. If Gender is “F” and Fmax > Mathematics marks then store Mathematics marks in Fmax
Step 7. Move the current card to another pile called Pile 2 and repeat from step 3
a. Step 1
b. Step 2
c. Step 3
d. Step 4
e. Step 5
f. Step 6
g. Step 7
h. No mistake
Answers
Answer:
Q1. Which of the following step / assumption in regression modeling impacts the trade-off between under-fitting and over-fitting the most.
A. The polynomial degree
B. Whether we learn the weights by matrix inversion or gradient descent
C. The use of a constant-term
Solution: A
Choosing the right degree of polynomial plays a critical role in fit of regression. If we choose higher degree of polynomial, chances of overfit increase significantly.
Q2. Suppose you have the following data with one real-value input variable & one real-value output variable. What is leave-one out cross validation mean square error in case of linear regression (Y = bX+c)?

A. 10/27
B. 20/27
C. 50/27
D. 49/27
Solution: D
We need to calculate the residuals for each cross validation point. After fitting the line with 2 points and leaving 1 point for cross validation.

Leave one out cross validation mean square error = (2^2 +(2/3)^2 +1^2) /3 = 49/27
Q3. Which of the following is/ are true about “Maximum Likelihood estimate (MLE)”?
MLE may not always exist
MLE always exists
If MLE exist, it (they) may not be unique
If MLE exist, it (they) must be unique
A. 1 and 4
B. 2 and 3
C. 1 and 3
D. 2 and 4
Solution: C
The MLE may not be a turning point i.e. may not be a point at which the first derivative of the likelihood (and log-likelihood) function vanishes.

* The MLE may not be unique.

Q4. Let’s say, a “Linear regression” model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
A. You will always have test error zero
B. You can not have test error zero
C. None of the above
Solution: C
Test error may be zero if there no noise in test data. In other words, it will be zero, if the test data is perfect representative of train data but not always.
Q5. In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.
Which of the following option is true?
A. If R Squared increases, this variable is significant.
B. If R Squared decreases, this variable is not significant.
C. Individually R squared cannot tell about variable importance. We can’t say anything about it right now.
D. None of these.
Answer:
step 5 and step 6
Step-by-step explanation: