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 options will be the output of the following code block? from sklearn.feature_extraction.text import TfidfVectorizer corpus = [ 'This is the first document.', 'And is this the second one?', ] vectorizer = TfidfVectorizer() X = vectorizer.fit_transform(corpus) vectorizer.get_feature_names_out()

(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

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

ROC curves A, B and C plotted as true positive rate vs false positive rate (Figure 2)

(B) The classifier shown in ‘Curve A’ is better than the other two classifiers.

(A) The classifier shown in ‘Curve B’ is better than the other two classifiers.

(C) The classifier shown in ‘Curve C’ is better than the other two classifiers.

(D) All 3 curves demonstrate 3 classifiers of the same quality.

What is the output of the following block of code? import numpy as np from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler X = np.array([[2, 4], [4, 2]]) y = np.array([-1, 1]) from sklearn.svm import SVC clf = make_pipeline(StandardScaler(), SVC(kernel='linear')) clf.fit(X, y) print(clf.predict([[-2,2]]))

(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

Consider the following code snippet and mark the correct output. from sklearn.svm import SVC import numpy as np X = np.array([[-1, -1], [-2, -2], [1, 1], [2, 2]]) y = np.array([-1, -1, 1, 1]) clf = SVC(kernel='linear') clf.fit(X, y) print(clf.n_support_)

(C) Option figure from the original question paper

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(D) Option figure from the original question paper

SVC training code with a scatter plot of Class 0 and Class 1 points on X1-X2 axes (Figure 4) What is the output that Prashant will get?

(D) ndarray of support vectors.

(A) Indices of support vectors.

(C) Weights assigned to the features when kernel=”linear”

May 2022 term · Machine Learning Practice · BSCS2008

Machine Learning Practice Quiz 2: 10 July 2022 (May 2022 term)

The IIT Madras BS Machine Learning Practice (MLP) Quiz 2 paper sat on 10 Jul 2022, in the May 2022 term: 21 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: 21
Marks: 50
Duration: 120 min
MCQ: 16
MSQ: 1
Numerical: 4

Take as mock test Practise with answers

Updated 25 Sept 2026

Official paper: IIT M DIPLOMA QUIZ2 EXAM QPE1 10 July 2022 · No negative marking.

Question 1

+1 markOne correct option

Which of the following vectorizer techniques can help to convert a large collection of text documents to a matrix?

A
CountVectorizer
B
HashingVectorizer
C
Both CountVectorizer and HashingVectorizer
D
None of these

Show answer

Correct answer

B
HashingVectorizer

Question 1 on its own page

Question 2

+2 marksOne correct option

Show answer

Correct answer

Question 2 on its own page

Question 3

+2 marksOne correct option

A
0.0
B
1.0
C
-1.0
D
0.5

Show answer

Correct answer

B
1.0

Question 3 on its own page

Question 4

+2 marksOne correct option

Which of the options below represent the correct method to preprocess class labels in multi-class setup?

Show answer

Correct answer

Question 4 on its own page

Question 5

+2 marksOne correct option

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

python

1from sklearn.feature_extraction.text import TfidfVectorizer
2corpus = [
3    'This is the first document.',
4    'And is this the second one?',
5    ]
6vectorizer = TfidfVectorizer()
7X = vectorizer.fit_transform(corpus)
8vectorizer.get_feature_names_out()

Show answer

Correct answer

Question 5 on its own page

Question 6

+2 marksOne correct option

A
0.65
B
0.73
C
0.37
D
None of these

Show answer

Correct answer

B
0.73

Question 6 on its own page

Question 7

+2 marksOne correct option

Rakesh is solving a regression problem with a KNN model. He has considered k = 3 for his model. Based on the graph shown below, what would you suggest him out of the following options?

A
His model is suffering from overfitting. So, he should increase the value of k.
B
His model is suffering from underfitting. So, he should increase the value of k.
C
His model is suffering from overfitting. So, he should decrease the value of k.
D
His model is suffering from underfitting. So, he should decrease the value of k.
E
No suggestions, Rakesh has already chosen the best value of k.

Show answer

Correct answer

A
His model is suffering from overfitting. So, he should increase the value of k.

Question 7 on its own page

Question 8

+2 marksOne correct option

l1 penalty in LinearSVC classifier leads to coef_vectors which are-

A
dense.
B
sparse.
C
Unique class labels.
D
l1 penalty can’t be set via a parameter in LinearSVC.

Show answer

Correct answer

B
sparse.

Question 8 on its own page

Question 9

+3 marksOne correct option

A
1
B
0
C
0.5
D
0.40

Show answer

Correct answer

C
0.5

Question 9 on its own page

Question 10

+3 marksOne correct option

Show answer

Correct answer

Question 10 on its own page

Question 11

+3 marksOne correct option

A
The classifier shown in ‘Curve B’ is better than the other two classifiers.
B
The classifier shown in ‘Curve A’ is better than the other two classifiers.
C
The classifier shown in ‘Curve C’ is better than the other two classifiers.
D
All 3 curves demonstrate 3 classifiers of the same quality.

Show answer

Correct answer

B
The classifier shown in ‘Curve A’ is better than the other two classifiers.

Question 11 on its own page

Question 12

+3 marksOne correct option

For a text classification task, which one of the following code snippets will be better suitable than the others?

Show answer

Correct answer

Question 12 on its own page

Question 13

+3 marksOne correct option

Which of the following will be the correct output of the code snippet given below?

python

1from sklearn.neighbors import KNeighborsClassifier
2X = [[2,3], [5,6], [10,11], [15,16],[20,21]]
3y = [0, 0, 1, 1, 2]
4neigh = KNeighborsClassifier(n_neighbors=3)
5neigh.fit(X, y)
6print(neigh.predict([[8,9]]))

A
0
B
1
C
2
D
None

Show answer

Correct answer

Question 13 on its own page

Question 14

+3 marksOne correct option

What is the output of the following block of code?

python

1import numpy as np
2from sklearn.pipeline import make_pipeline
3from sklearn.preprocessing import StandardScaler
4X = np.array([[2, 4], [4, 2]])
5y = np.array([-1, 1])
6from sklearn.svm import SVC
7clf = make_pipeline(StandardScaler(), SVC(kernel='linear'))
8clf.fit(X, y)
9print(clf.predict([[-2,2]]))

Show answer

Correct answer

Question 14 on its own page

Question 15

+3 marksOne correct option

Consider the following code snippet and mark the correct output.

python

1from sklearn.svm import SVC
2import numpy as np
3X = np.array([[-1, -1], [-2, -2], [1, 1], [2, 2]])
4y = np.array([-1, -1, 1, 1])
5clf = SVC(kernel='linear')
6clf.fit(X, y)
7print(clf.n_support_)

Show answer

Correct answer

Question 15 on its own page

Question 16

+3 marksOne or more correct options

Which of the following code blocks will correctly train a large scale regression model using partial_fit, if shape of X_train,Y_train are given as (8000,900,10) and (8000,900) respectively. Note that the dataset has been transformed into batches of size 900 each?

Select all that apply.