What will be the output of the following code? Figure from the original question paper

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

Which of the following is likely to be the correct output of the code given below? Figure from the original question paper

(C) No evaluation metrics are mentioned, hence it will produce an error

Which of the following is likely to be the correct output of the code given below? from math import nan import numpy as np from sklearn.impute import SimpleImputer data=[(2,3),(4,5),(3,1),(1,2),(5, nan)] imputer = SimpleImputer(missing_values = np.nan, strategy ='mean', add_indicator=True) imputer = imputer.fit(data) data_imputed_with_indicator = imputer.transform(data) print (data_imputed_with_indicator)

(B) Option figure from the original question paper

(A) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

Consider the following code snippet: Figure from the original question paper Which of the following could be the correct output?

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

Which of the following options will be the correct output of print(clf.coef_)? Figure from the original question paper

(D) Given code block will return an error.

Which of the following is likely to be the correct output of the code given below? Figure from the original question paper

(D) Option figure from the original question paper

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

Go through the code snippet given below and answer the given subquestions. import numpy as np from sklearn.linear_model import SGDRegressor from sklearn.pipeline import make_pipeline n_samples, n_features = 18, 4 rng = np.random.RandomState(0) y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) reg = SGDRegressor(max_iter=1000,tol=1e-3,eta0= 0.04, power_t=5, n_iter_no_change=3, validation_fraction=0.3 ,random_state=42) reg.fit(X, y) print(reg.coef_) Which of the following options will be the output of the given code?

(A) [-0.02634908 0.01189399 0.0917284 0.08966849]

(B) array([-0.22622766, -0.00582008, -0.1820344 , 0.03518086, -0.14490955])

(C) array([-0.22622766, -0.00582008, -0.1820344 ])

(D) Given code will return an error because the data set is not given

Go through the code snippet given below and answer the given subquestions. import numpy as np from sklearn.linear_model import SGDRegressor from sklearn.pipeline import make_pipeline n_samples, n_features = 18, 4 rng = np.random.RandomState(0) y = rng.randn(n_samples) X = rng.randn(n_samples, n_features) reg = SGDRegressor(max_iter=1000,tol=1e-3,eta0= 0.04, power_t=5, n_iter_no_change=3, validation_fraction=0.3 ,random_state=42) reg.fit(X, y) print(reg.coef_) Which of the following could be the possible output of print(reg.score())?

(D) Given code will return an error

September 2022 term · Machine Learning Practice · BSCS2008

Machine Learning Practice Quiz 1: 16 October 2022 (September 2022 term)

The IIT Madras BS Machine Learning Practice (MLP) Quiz 1 paper sat on 16 Oct 2022, in the September 2022 term: 20 questions for 50 marks in 120 minutes. Every question is below with its answer. Take it as a timed mock test to be marked, or read it through first.

Questions: 20
Marks: 50
Duration: 120 min
MCQ: 16
MSQ: 3
Numerical: 1

Take as mock test Practise with answers

Updated 25 Sept 2026

Official paper: IIT M QUIZ 1 FOUNDATION DAD DIPLOMA QPD2 16 Oct 2022 · No negative marking.

Question 1

+1 markOne correct option

The violin plot shown in Figure 2 demonstrates the age of passengers on the Titanic ship. Mark the approximate age range of most of the passengers.

A
20 - 40
B
80 above
C
Less than 20
D
40-60

Show answer

Correct answer

A
20 - 40

Question 1 on its own page

Question 2

+2 marksOne correct option

Which of the following Sklearn objects can help you to reduce, expand or generate feature representations?

A
Estimator
B
Predictor
C
Transformer
D
None of these

Show answer

Correct answer

C
Transformer

Question 2 on its own page

Question 3

+2 marksOne correct option

What will be the output of the following code?

Show answer

Correct answer

Question 3 on its own page

Question 4

+2 marksOne correct option

Go through the code snippet given below:

What will be the shape of the feature matrix (X.shape) and the label vector (y.shape)?

A
(2000, 12) and (1000, 6)
B
(1, 1000) and (1,1000)
C
(599, 1) and (699, 1)
D
(1000, 6) and (1000,)

Show answer

Correct answer

D
(1000, 6) and (1000,)

Question 4 on its own page

Question 5

+2 marksOne correct option

Which of the following options is likely to be the correct output of the code snippet given below?

Show answer

Correct answer

Question 5 on its own page

Question 6

+2 marksOne correct option

In the figure 1, what are the names of classes arranged in the increasing order of the median values?

A
First < Second < Third
B
Third < Second < First
C
Second < Third < First
D
Third < First < Second

Show answer

Correct answer

B
Third < Second < First

Question 6 on its own page

Question 7

+2 marksOne correct option

Go through the code snippet given below and mark the correct output.

A
1
B
3
C
9
D
25
E
4
F
5

Show answer

Correct answer

Question 7 on its own page

Question 8

+2 marksOne correct option

Which of the following is likely to be the correct output of the code given below?

A
12
B
29
C
No evaluation metrics are mentioned, hence it will produce an error
D
0.96

Show answer

Correct answer

D
0.96

Question 8 on its own page

Question 9

+3 marksOne correct option

Which of the following is likely to be the correct output of the code given below?

python

1from math import nan
2import numpy as np
3from sklearn.impute import SimpleImputer
4data=[(2,3),(4,5),(3,1),(1,2),(5, nan)]
5imputer = SimpleImputer(missing_values = np.nan, strategy ='mean',
6                        add_indicator=True)
7imputer = imputer.fit(data)
8data_imputed_with_indicator = imputer.transform(data)
9print (data_imputed_with_indicator)

Show answer

Correct answer

Question 9 on its own page

Question 10

+3 marksOne correct option

Consider the following code snippet:

Which of the following could be the correct output?

Show answer

Correct answer

Question 10 on its own page

Question 11

+3 marksOne correct option

Which of the following options will be the correct output of print(clf.coef_)?

A
[ 0.35 -0. -0. ]
B
[-0.85,0]
C
[3,2,1,2]
D
Given code block will return an error.

Show answer

Correct answer

A
[ 0.35 -0. -0. ]

Question 11 on its own page

Question 12

+3 marksOne correct option

Go through the code snippet given below and mark the correct output.

A
0.61
B
0.83
C
0.95
D
1
E
-0.6
F
5

Show answer

Correct answer

C
0.95

Question 12 on its own page

Question 13

+3 marksOne correct option

Which of the following is likely to be the correct output of the code given below?

Show answer

Correct answer

Question 13 on its own page

Question 14

+3 marksOne correct option

Which of the following code blocks will correctly take the learning rate as‘optimal’ ?

Show answer

Correct answer

Question 14 on its own page

Question 15

+2 marksOne or more correct options

Which of the following code blocks will correctly return an array of feature variables?

Select all that apply.

Show answer

Correct answers

Question 15 on its own page

Question 16

+3 marksOne or more correct options

Scikit-Learn’s API is remarkably well designed. The main design principles are:-

Select all that apply.

A
Nonproliferation of classes
B
Sensible defaults
C
Consistency
D
Proportion

Show answer

Correct answers

A
Nonproliferation of classes
B
Sensible defaults
C
Consistency

Question 16 on its own page

Question 17

+3 marksOne or more correct options

Go through the code block given below:

python

1from sklearn.preprocessing import PolynomialFeatures
2from sklearn.pipeline import Pipeline
3from sklearn.preprocessing import StandardScaler
4from sklearn.linear_model import RidgeCV
5from sklearn.datasets import load_diabetes
6diabetes = load_diabetes(as_frame=True)
7train_X= diabetes.data
8train_y= diabetes.target
9
10lf= np.logspace(-5, 0, num=6)
11
12reg= Pipeline([("poly", PolynomialFeatures(degree=2)),
13                            ("feature_scaling", StandardScaler())
14                            ])
15
16ridge= RidgeCV(alphas=lf,scoring= None,fit_intercept = False)
17results = ridge.fit(train_X, train_y)
18print(results.intercept_)

Which of the following can’t be the correct output of the given code block?

Select all that apply.

A
0.0
B
0.01
C
0.1
D
1

Show answer

Correct answers

B
0.01
C
0.1
D
1

Question 17 on its own page

Question 18

+3 marksNumerical answer

Enter the correct output of the following code block.

python

1import numpy as np
2from sklearn.model_selection import ShuffleSplit
3X = np.array([[2, 2], [5, 4], [1, 6], [2, 8], [3, 4], [2, 6]])
4y = np.array([3, 2, 1, 2, 3, 2])
5rs = ShuffleSplit(n_splits=5, test_size=.25, random_state=0)
6k=1
7for i,j in rs.split(X):
8  k+=1
9print(k)

Show answer

Correct answer: 6

Question 18 on its own page

Question 19

+3 marksOne correct option

Go through the code snippet given below and answer the given subquestions.

python

1import numpy as np
2from sklearn.linear_model import SGDRegressor
3from sklearn.pipeline import make_pipeline
4n_samples, n_features = 18, 4
5rng = np.random.RandomState(0)
6y = rng.randn(n_samples)
7X = rng.randn(n_samples, n_features)
8reg = SGDRegressor(max_iter=1000,tol=1e-3,eta0= 0.04, power_t=5,
9                   n_iter_no_change=3, validation_fraction=0.3 ,random_state=42)
10reg.fit(X, y)
11print(reg.coef_)

Which of the following options will be the output of the given code?

A
[-0.02634908 0.01189399 0.0917284 0.08966849]
B
array([-0.22622766, -0.00582008, -0.1820344 , 0.03518086, -0.14490955])
C
array([-0.22622766, -0.00582008, -0.1820344 ])
D
Given code will return an error because the data set is not given

Show answer

Correct answer

A
[-0.02634908 0.01189399 0.0917284 0.08966849]

Question 19 on its own page

Question 20

+3 marksOne correct option

Go through the code snippet given below and answer the given subquestions.

python

1import numpy as np
2from sklearn.linear_model import SGDRegressor
3from sklearn.pipeline import make_pipeline
4n_samples, n_features = 18, 4
5rng = np.random.RandomState(0)
6y = rng.randn(n_samples)
7X = rng.randn(n_samples, n_features)
8reg = SGDRegressor(max_iter=1000,tol=1e-3,eta0= 0.04, power_t=5,
9                   n_iter_no_change=3, validation_fraction=0.3 ,random_state=42)
10reg.fit(X, y)
11print(reg.coef_)

Which of the following could be the possible output of print(reg.score())?

A
-0.528
B
1
C
0.528
D
Given code will return an error

Show answer

Correct answer

D
Given code will return an error

Question 20 on its own page