Consider following code snippet: from sklearn.utils.multiclass import type_of_target import numpy as np print(type_of_target(np.array([['horror','fantasy'], ['adventure','fantasy'], ['adventure','fantasy']]))) print(type_of_target([72, 17.89, 63.00])) print(type_of_target([0, 1, 1, 0])) What will be the output of the above code snippet in the correct sequence?

(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

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

Consider the following code block: from sklearn.datasets import make_regression X, y = make_regression(n_samples = 1000, n_features = 5, n_informative = 2, random_state=42) from sklearn.linear_model import SGDRegressor sgd1 = SGDRegressor(alpha=1e-3, random_state=42, penalty='', ) sgd1.fit(X, y) print(sgd1.coef_) sgd2 = SGDRegressor(alpha=1e-3, random_state=42, penalty='_') sgd2.fit(X, y) print(sgd2.coef_) What are the most suitable values to be filled in the two blank spaces (in that order) in the code to expect the following output?: [ 1.68059576e+01, 1.89752021e+01, 7.49212536e-04, -6.53455275e-04, 3.01471918e-04] [16.82258106, 18.99248887, 0., 0., 0.]

(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

You’re building a machine learning pipeline to preprocess data and train a model on a classification task. You decide to use a pipeline that includes data preprocessing and a support vector machine (SVM) classifier. The following code snippet demonstrates the pipeline creation and usage: from sklearn.pipeline import Pipeline from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler import numpy as np # Simulated data (features: X, target: y) X = np.array([[2, 3], [5, 7], [8, 10]]) y = np.array([0, 1, 0]) # Create a pipeline with StandardScaler and SVM classifier pipeline = Pipeline([('scaler', StandardScaler()), ('svm', SVC())]) # Fit the pipeline on training data pipeline.fit(X, y) # Make predictions using the trained pipeline predictions = pipeline.predict(X) What is the purpose of using the pipeline in this code snippet?

(C) The pipeline simplifies the code by encapsulating preprocessing and modeling steps.

(A) The pipeline combines multiple models for better model performance.

(B) The pipeline allows for simultaneous training of the scaler and classifier.

(D) The pipeline ensures that only linear SVM can be used for this classification task.

Given below code to load a huge file name as filename.csv and this file is not loading at once in the system which parameter should be added to pd.read_csv to load this file ? import pandas as pd from sklearn.linear_model import SGDRegressor for train_df in pd.read_csv("filename.csv", =1024): X = train_df.iloc[:, :-1] y = train_df.iloc[:, -1] model = SGDRegressor() model.partial_fit(prep_X,y)

(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

What will the output for below code from sklearn.feature_extraction.text import CountVectorizer corpus = [ 'This is the first document.', 'This document is the second document.'] vectorizer = CountVectorizer() vectorizer.fit_transform(corpus) print(vectorizer.get_feature_names_out())

(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

Imagine you're training a Perceptron using sklearn with the following code: from sklearn.linear_model import Perceptron X = [[0, 0.5], [1, 1.5], [1, 2], [2, 3]] y = [-1, -1, 1, 1] clf = Perceptron(eta0 = 1, tol=None, shuffle=True, random_state=42) clf.fit(X, y) iterations = clf.n_iter_ Given the linearly separable nature of the data, how many iterations would it most likely take for the perceptron to converge? What will be the value of iterations?

(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

Consider the following code snippet using scikit-learn: from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn.svm import SVC from sklearn.model_selection import GridSearchCV pipeline = Pipeline([('scaler', StandardScaler()), ('classifier', SVC())]) param_grid = {'scalerwith_mean': [True, False], 'classifierC': [0.1, 1, 10], 'classifierkernel': ['linear', 'rbf'], 'classifiergamma': [0.1, 1, 10]} grid_search = GridSearchCV(estimator= pipeline, param_grid= param_grid, cv=5, scoring='accuracy', verbose=2) grid_search.fit(X_train, y_train) Assuming that X_train and y_train are given and the features are not sparse, which of the following statements about the given code is correct?

(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 that employs LogisticRegression from sklearn on a feature matrix X and corresponding label vector y: from sklearn.linear_model import LogisticRegression model = LogisticRegression(class_weight='balanced', C=0.5) model.fit(X, y) Given the code above, which of the following statements is true?

(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

Figure from the original question paper

(C) Option 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

Figure from the original question paper

(A) Option figure from the original question paper

(D) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

January 2024 term · Machine Learning Practice · BSCS2008

Machine Learning Practice Quiz 2: 24 March 2024 (January 2024 term)

The IIT Madras BS Machine Learning Practice (MLP) Quiz 2 paper sat on 24 Mar 2024, in the January 2024 term: 22 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: 22
Marks: 50
Duration: 120 min
MCQ: 12
MSQ: 6
Numerical: 4

Take as mock test Practise with answers

Updated 25 Sept 2026

Official paper: IIT M DIPLOMA AN EXAM QDD2 24 Mar 2024 · No negative marking.

Question 1

+2 marksOne correct option

Consider following code snippet:

python

1from sklearn.utils.multiclass import type_of_target
2import numpy as np
3print(type_of_target(np.array([['horror','fantasy'],
4                               ['adventure','fantasy'],
5                               ['adventure','fantasy']])))
6print(type_of_target([72, 17.89, 63.00]))
7print(type_of_target([0, 1, 1, 0]))

What will be the output of the above code snippet in the correct sequence?

Show answer

Correct answer

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

Consider a binary classification dataset with labeled as 98% negative samples and 2% positive samples. A model is trained on this data, which of the following evaluation metrics are suitable for measuring effectiveness of this model:

A
accuracy
B
Mean Absolute Error
C
smote
D
F-1 score

Show answer

Correct answer

D
F-1 score

Question 3 on its own page

Question 4

+2 marksOne correct option

Consider the following code block:

python

1from sklearn.datasets import make_regression
2X, y = make_regression(n_samples = 1000,
3                       n_features = 5,
4                       n_informative = 2,
5                       random_state=42)
6
7from sklearn.linear_model import SGDRegressor
8sgd1 = SGDRegressor(alpha=1e-3,
9                    random_state=42,
10                    penalty='________________', )
11sgd1.fit(X, y)
12print(sgd1.coef_)
13
14sgd2 = SGDRegressor(alpha=1e-3,
15                    random_state=42,
16                    penalty='_________________')
17sgd2.fit(X, y)
18print(sgd2.coef_)

What are the most suitable values to be filled in the two blank spaces (in that order) in the code to expect the following output?:

[ 1.68059576e+01, 1.89752021e+01, 7.49212536e-04, -6.53455275e-04, 3.01471918e-04]

[16.82258106, 18.99248887, 0., 0., 0.]

Show answer

Correct answer

Question 4 on its own page

Question 5

+2 marksOne correct option

A
0.00
B
0.50
C
0.72
D
1.00

Show answer

Correct answer

B
0.50

Question 5 on its own page

Question 6

+2 marksOne correct option

You’re building a machine learning pipeline to preprocess data and train a model on a classification task. You decide to use a pipeline that includes data preprocessing and a support vector machine (SVM) classifier. The following code snippet demonstrates the pipeline creation and usage:

python

1from sklearn.pipeline import Pipeline
2from sklearn.svm import SVC
3from sklearn.preprocessing import StandardScaler
4import numpy as np
5
6# Simulated data (features: X, target: y)
7X = np.array([[2, 3], [5, 7], [8, 10]])
8y = np.array([0, 1, 0])
9
10# Create a pipeline with StandardScaler and SVM classifier
11pipeline = Pipeline([('scaler', StandardScaler()),
12                     ('svm', SVC())])
13
14# Fit the pipeline on training data
15pipeline.fit(X, y)
16
17# Make predictions using the trained pipeline
18predictions = pipeline.predict(X)

What is the purpose of using the pipeline in this code snippet?

A
The pipeline combines multiple models for better model performance.
B
The pipeline allows for simultaneous training of the scaler and classifier.
C
The pipeline simplifies the code by encapsulating preprocessing and modeling steps.
D
The pipeline ensures that only linear SVM can be used for this classification task.

Show answer

Correct answer

C
The pipeline simplifies the code by encapsulating preprocessing and modeling steps.

Question 6 on its own page

Question 7

+2 marksOne correct option

Given below code to load a huge file name as filename.csv and this file is not loading at once in the system which parameter should be added to pd.read_csv to load this file ?

python

1import pandas as pd
2from sklearn.linear_model import SGDRegressor
3for train_df in pd.read_csv("filename.csv", __________=1024):
4    X = train_df.iloc[:, :-1]
5    y = train_df.iloc[:, -1]
6    model = SGDRegressor()
7    model.partial_fit(prep_X,y)

Show answer

Correct answer

Question 7 on its own page

Question 8

+2 marksOne correct option

What will the output for below code

python

1from sklearn.feature_extraction.text import CountVectorizer
2corpus = [ 'This is the first document.',
3           'This document is the second document.']
4vectorizer = CountVectorizer()
5vectorizer.fit_transform(corpus)
6print(vectorizer.get_feature_names_out())

Show answer

Correct answer

Question 8 on its own page

Question 9

+2 marksOne correct option

Imagine you're training a Perceptron using sklearn with the following code:

python

1from sklearn.linear_model import Perceptron
2X = [[0, 0.5], [1, 1.5], [1, 2], [2, 3]]
3y = [-1, -1, 1, 1]
4clf = Perceptron(eta0 = 1, tol=None, shuffle=True, random_state=42)
5clf.fit(X, y)
6iterations = clf.n_iter_

Given the linearly separable nature of the data, how many iterations would it most likely take for the perceptron to converge? What will be the value of iterations?

Show answer

Correct answer

Question 9 on its own page

Question 10

+2 marksOne correct option

Consider the following code snippet using scikit-learn:

python

1from sklearn.preprocessing import StandardScaler
2from sklearn.pipeline import Pipeline
3from sklearn.svm import SVC
4from sklearn.model_selection import GridSearchCV
5
6pipeline = Pipeline([('scaler', StandardScaler()),
7                     ('classifier', SVC())])
8
9param_grid = {'scaler__with_mean': [True, False],
10              'classifier__C': [0.1, 1, 10],
11              'classifier__kernel': ['linear', 'rbf'],
12              'classifier__gamma': [0.1, 1, 10]}
13
14grid_search = GridSearchCV(estimator= pipeline,
15                           param_grid= param_grid,
16                           cv=5,
17                           scoring='accuracy',
18                           verbose=2)
19grid_search.fit(X_train, y_train)

Assuming that X_train and y_train are given and the features are not sparse, which of the following statements about the given code is correct?

Show answer

Correct answer

Question 10 on its own page

Question 11

+2 marksOne correct option

Consider the following code snippet that employs LogisticRegression from sklearn on a feature matrix X and corresponding label vector y:

python

1from sklearn.linear_model import LogisticRegression
2model = LogisticRegression(class_weight='balanced', C=0.5)
3model.fit(X, y)

Given the code above, which of the following statements is true?

Show answer

Correct answer

Question 11 on its own page

Question 12

+2 marksOne correct option

Which of the following is true for a hard margin SVM algorithm ?

A
It does not create hyperplanes as a classification decision boundary
B
It is robust to outliers
C
It will correctly classify all the datapoints if the data is linearly separable.
D
It is mostly used for clustering the data

Show answer

Correct answer

C
It will correctly classify all the datapoints if the data is linearly separable.

Question 12 on its own page

Question 13

+3 marksOne or more correct options

Which of the following is/are correct regarding RadiusNeighborsClassifier

Select all that apply.

Show answer

Correct answers

Question 13 on its own page

Question 14

+3 marksOne or more correct options

Which of the following is correct?

Select all that apply.

Show answer

Correct answers

Question 14 on its own page

Question 15

+3 marksOne or more correct options

Which of the following option(s) are correct for the precision-recall curve

Select all that apply.

A
A high area under the curve represents both high recall and high precision.
B
The precision-recall curve shows the trade-off between precision and recall for different threshold values.
C
The precision-recall curve used to evaluate unsupervised algorithm for imbalanced clustered data.
D
None of these

Show answer

Correct answers

A
A high area under the curve represents both high recall and high precision.
B
The precision-recall curve shows the trade-off between precision and recall for different threshold values.

Question 15 on its own page

Question 16

+3 marksOne or more correct options

Which of the following statements are true?

Select all that apply.

A
KNeighborsClassifier with low values of n_neighbors produces complex decision boundaries.
B
KNeighborsClassifier with low values of n_neighbors produces smooth decision boundaries.
C
In KNeighborsClassifier the scale of the features(columns) can impact the decision boundaries.
D
None of these

Show answer

Correct answers

A
KNeighborsClassifier with low values of n_neighbors produces complex decision boundaries.
C
In KNeighborsClassifier the scale of the features(columns) can impact the decision boundaries.

Question 16 on its own pageAlso asked in End Term 3 Sept 2023

Question 17

+3 marksOne or more correct options

Select all that apply.

Show answer

Correct answers

Question 17 on its own page

Question 18

+2 marksOne or more correct options

Select all that apply.

Show answer

Correct answers

Question 18 on its own page

Question 19

+2 marksNumerical answer

Consider the following code snippet:

python

1from sklearn.datasets import load_iris
2from sklearn.decomposition import PCA
3from sklearn.preprocessing import PolynomialFeatures
4from sklearn.pipeline import FeatureUnion
5X = load_iris().data # X.shape = (150,4)
6
7poly_feature = PolynomialFeatures(degree=2, include_bias=True)
8union = FeatureUnion([('poly', poly_feature),
9                      ('pca', PCA(n_components=2))])
10
11X_transformed = union.fit_transform(X)
12print(X_transformed.shape)

If the shape of X is $(150, 4)$ . How many total columns are there in the X_transformed ?

Show answer

Correct answer: 17

Question 19 on its own page

Question 20

+2 marksNumerical answer

Please consider the following data and code for a regression problem with symbols in mind:

>>>: Represents input code
# : Represents comment in a code
... : Represents code continuation
Without any symbols at the beginning of a line then it is output of just above input line of code.

python

1>>> import pandas as pd
2>>> from sklearn.preprocessing import OneHotEncoder
3>>> from sklearn.linear_model import LinearRegression
4>>> data_array = [[19, 'Black', 74],
5...               [19, 'Blue', 75],
6...               [19, 'Red', 85],
7...               [24, 'Black', 70],
8...               [24, 'Blue', 70],
9...               [24, 'Red', 89],
10...               [30, 'Black', 78],
11...               [30, 'Blue', 76],
12...               [30, 'Red', 90]]
13
14>>> data = pd.DataFrame(data_array,columns=["Age",
15                                            "Car_color",
16                                            "Accidents_per_1000_Driver"])
17
18>>> X = data.drop("Accidents_per_1000_Driver", axis=1)
19>>> y = data["Accidents_per_1000_Driver"]
20
21>>> ohe = OneHotEncoder(sparse_output=False)
22>>> X[['Black', 'Blue', 'Red']] = ohe.fit_transform(X[["Car_color"]])
23>>> X.drop("Car_color", axis=1, inplace=True)
24
25>>> lr = LinearRegression().fit(X, y)
26
27>>> print(lr.coef_)
28[0.32, -4.55, -4.88, 9.44]
29
30>>> print(lr.intercept_)
3170.75

How many Accidents per 1000 Driver predicted by the model for Age 27 and driving a Red car ?

Show answer

Correct answer: 88.8 (accepted within ±0.5)

Question 20 on its own page

Question 21

+2 marksNumerical answer

After training a multi-class classifier, you obtain the following confusion matrix. What will be the weighted average of the recall score for each class?

Show answer

Correct answer: 0.305 (accepted within ±0.01)

Question 21 on its own page

Question 22

+3 marksNumerical answer

What is the output of the following code?

python

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

Show answer

Correct answer: 1

Question 22 on its own page