Consider the following common data and answer the subquestion: Figure from the passage in the original paper Consider the following code snippet: Figure from the original question paper Which of the following will be equivalent to the above code snippet?

(A) Option figure from the original question paper

(C) Option figure from the original question paper

(B) Option figure from the original question paper

(D) Option figure from the original question paper

(E) Option figure from the original question paper

Consider the following common data and answer the subquestion: Figure from the passage in the original paper What is the output of the following code snippet: Figure from the original question paper Choose correct options from following:

(A) The number of rows will decrease in the dataset.

(B) The number of columns will decrease in the dataset.

(C) The number of columns and number of rows, both, will decrease in the dataset.

Consider the following code block: X = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i'] from sklearn.model_selection import KFold kf = KFold(n_splits = 3) for train, test in kf.split(X): print(train, test) Which of the following may be the correct output of the above code?

(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

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 could be 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.vocabulary_)

(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

Following is the code to tune the degree parameter of a polynomial regression model. from sklearn.model_selection import GridSearchCV from sklearn.pipeline import Pipeline from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import SGDRegressor param_grid = [{_: [2, 3, 4, 5, 6, 7, 8, 9]}] pipeline = Pipeline(steps=[('poly', PolynomialFeatures()), ('sgd', SGDRegressor())]) grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='neg_mean_squared_error', return_train_score=True) grid_search.fit(X_train, y_train) What should the blank space contain?

(E) 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

(D) Option figure from the original question paper

Figure from the original question paper

(B) It shrinks the contribution of each classifier.

(A) It controls the number of weak learners.

(C) It sets the weight of each weak learner.

(D) It adjusts the speed at which the model learns.

The following code produces an output of 0.9125. How is the output expected to change if we increase the max_depth value?: from sklearn.datasets import load_wine from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split X,y = load_wine(as_frame = True, return_X_y = True) X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.10, random_state = 12) clf = DecisionTreeClassifier(max_depth = 2, min_samples_split = 2, min_samples_leaf=3, random_state = 81) clf.fit(X_train, y_train) print(clf.score(X_train, y_train))

(A) Output score is likely to increase.

(B) Output score is likely to decrease.

(C) Output score may increase or decrease.

(D) Output score will remain the same.

Consider the following code for a VotingClassifier. What is the effect of setting voting='soft'? from sklearn.ensemble import VotingClassifier clf1 = LogisticRegression() clf2 = RandomForestClassifier() clf3 = SVC(probability=True) eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('svc', clf3)], voting='soft') eclf.fit(X_train, y_train)

(B) The final predictions are based on the average of probabilities predicted by each classifier.

(A) The final predictions are based on the majority vote.

(C) The final predictions are based on the weighted sum of the predictions.

(D) The final predictions are based on the classifier with the highest accuracy.

Figure from the original question paper

(A) The mean accuracy across all cross-validation folds.

(B) The mean precision across all cross-validation folds.

(C) The mean recall across all cross-validation folds.

(D) The mean F1 score across all cross-validation folds.

Figure from the original question paper

(A) Plotting the sum of the squared distances of samples to their closest cluster center

(B) by Changing the distance metric for KMeans

(C) By initialising clustering centroids very far from each other

Consider the following code block: from sklearn.linear_model import linear_regression from sklearn.model_selection import cross_val_score from sklearn.model_selection import ShuffleSplit lin_reg = linear_regression() shuffle_split = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42) score = cross_val_score(lin_reg, X, y, cv=shuffle_split, scoring='------------------------') Which of the following may be appropriate to be filled in the blank space?

(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

Figure from the original question paper

(A) Data is continuously being generated

(B) Data is generated every month

(C) Whole data is generated and its in a huge file size

Figure from the original question paper

(C) The neural network contains 2 hidden layers with 3 neurons in the second hidden layer

(D) The neural network contains 2 hidden layers with 5 neurons in the first hidden layer

(A) The neural network contains 3 hidden layers with 5 neurons in each hidden layer

(B) The neural network contains 5 hidden layers with 3 neurons in each hidden layer

(E) All of the given options are correct

Consider the following block of code: from sklearn.datasets import load_breast_cancer from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split X,y = load_breast_cancer(as_frame = True, return_X_y = True) X_train,X_test,y_train,y_test = train_test_split(X,y, test_size = 0.2, random_state=42) clf = DecisionTreeClassifier(min_samples_split = 6, min_samples_leaf = 4, random_state = 5) clf.fit(X_train, y_train) print(clf.score(X_test, y_test)) In which of the following scenarios, the split will can happen at node N?

(A) Number of samples at node N = 15. If it is split, it will result in 9 samples in the left child node and 6 sample in the right child node.

(D) Number of samples at node N = 7. If it is split, it will result in 4 samples in the left child node and 3 samples in the right child node.

(B) Number of samples at node N = 5. If it is split, it will result in 4 samples in the left child node and 2 samples in the right child node.

(C) Number of samples at node N = 12. If it is split, it will result in 3 samples in the left child node and 9 samples in the right child node.

May 2024 term · Machine Learning Practice · BSCS2008

MLP End Term: 1 September 2024, Set QDF3 (May 2024 term)

The IIT Madras BS Machine Learning Practice (MLP) End Term paper sat on 1 Sept 2024, in the May 2024 term, set QDF3: 37 questions for 100 marks in 180 minutes. Every question is below with its answer. Take it as a timed mock test to be marked, or read it through first.

Questions: 37
Marks: 100
Duration: 180 min
MSQ: 13
Numerical: 5
MCQ: 19

Take as mock test Practise with answers

Updated 25 Sept 2026

Official paper: IIT M FOUNDATION DIPLOMA AN EXAM QDF3 01 Sep 2024 · No negative marking.

Question 1

+2 marksOne or more correct options

Consider the following common data and answer the subquestion:

Consider the following code snippet:

Which of the following will be equivalent to the above code snippet?

Select all that apply.

Show answer

Correct answers

Question 1 on its own page

Question 2

+2 marksNumerical answer

Consider the following common data and answer the subquestion:

What is the output of the following code snippet:

Enter -1, if you think the above statement will generate an error.

Show answer

Correct answer: 120

Question 2 on its own page

Question 3

+2 marksNumerical answer

Consider the following common data and answer the subquestion:

What is the output of the following code snippet:

Enter -1, if you think the above statement will generate an error.

Show answer

Correct answer: 3

Question 3 on its own page

Question 4

+2 marksNumerical answer

Consider the following common data and answer the subquestion:

What is the output of the following code snippet:

Enter -1, if you think the above statement will generate an error.

Show answer

Correct answer: -1

Question 4 on its own page

Question 5

+2 marksOne correct option

Consider the following common data and answer the subquestion:

What is the output of the following code snippet:

Choose correct options from following:

A
The number of rows will decrease in the dataset.
B
The number of columns will decrease in the dataset.
C
The number of columns and number of rows, both, will decrease in the dataset.
D
There will be no change.
E
Insufficient information.

Show answer

Correct answer

A
The number of rows will decrease in the dataset.

Question 5 on its own page

Question 6

+2 marksOne correct option

To load datasets from openml.org, which method will be appropriate?

A
load_openml()
B
read_openl()
C
read_data()
D
fetch_openml()
E
load_data()
F
load_csv()
G
fetch_csv()

Show answer

Correct answer

D
fetch_openml()

Question 6 on its own page

Question 7

+2 marksOne correct option

In which of the option given below data preprocessing is required?

A
In some columns which has values between 0 and 1.
B
A column contains entity names such as ‘Chennai’, ‘CHENNAI’ and ‘MADRAS’.
C
The data has only numbers in all the columns.

Show answer

Correct answer

B
A column contains entity names such as ‘Chennai’, ‘CHENNAI’ and ‘MADRAS’.

Question 7 on its own page

Question 8

+2 marksOne correct option

Consider the following code block:

python

1X = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']
2from sklearn.model_selection import KFold
3kf = KFold(n_splits = 3)
4for train, test in kf.split(X):
5  print(train, test)

Which of the following may be the correct output of the above code?

Show answer

Correct answer

Question 8 on its own page

Question 9

+2 marksOne correct option

Which of the following scikit-learn classes is best suited for examining how the number of samples impacts training and testing errors?

Show answer

Correct answer

Question 9 on its own page

Question 10

+2 marksOne correct option

Consider following two statements:
Statement 1: The GaussianNB classifier can incrementally learn using partial_fit.
Statement 2: GaussianNB performance suffers when features are dependent, as it assumes independence in calculating conditional probabilities.

A
Both statements are True
B
Only statement 1 is True
C
Only statement 2 is True
D
Both statements are False

Show answer

Correct answer

A
Both statements are True

Question 10 on its own page

Question 11

+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 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 decision boundary
B
It will correctly classify all the data point if the data is linearly separable
C
It is robust to outliers
D
It is mostly used for clustering the data

Show answer

Correct answer

B
It will correctly classify all the data point if the data is linearly separable

Question 12 on its own page

Question 13

+2 marksOne correct option

What could be 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.vocabulary_)

Show answer

Correct answer

Question 13 on its own page

Question 14

+2 marksOne correct option

Decision Trees are prone to:

A
Low bias, low variance
B
High bias, low variance
C
Low bias, high variance
D
High bias, high variance

Show answer

Correct answer

C
Low bias, high variance

Question 14 on its own page

Question 15

+3 marksOne correct option

Following is the code to tune the degree parameter of a polynomial regression model.

python

1from sklearn.model_selection import GridSearchCV
2from sklearn.pipeline import Pipeline
3from sklearn.preprocessing import PolynomialFeatures
4from sklearn.linear_model import SGDRegressor
5
6param_grid = [{_________: [2, 3, 4, 5, 6, 7, 8, 9]}]
7pipeline = Pipeline(steps=[('poly', PolynomialFeatures()),
8                           ('sgd', SGDRegressor())])
9grid_search = GridSearchCV(pipeline, param_grid, cv=5,
10scoring='neg_mean_squared_error',
11return_train_score=True)
12grid_search.fit(X_train, y_train)

What should the blank space contain?

Show answer

Correct answer

Question 15 on its own page

Question 16

+3 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 = 4,
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?:

python

1[48.50064306,  0    ,  0    , 8.53931469]
2[4.84494867e+01, -7.58443057e-04, -2.09844306e-03,  8.53220113e+00]

A
‘l1’, ‘l2’
B
‘l1’, None
C
‘l2’, ‘l1’
D
‘l2’, None

Show answer

Correct answer

A
‘l1’, ‘l2’

Question 16 on its own page

Question 17

+3 marksOne correct option

Consider following code:

python

1estimator = SGDClassifier(loss='log_loss',
2                          penalty='l2',
3                          max_iter=1,
4                          warm_start="_____",
5                          eta0=0.01,
6                          alpha=0,
7                          learning_rate='constant',
8                          )
9pipe_sgd= make_pipeline(MinMaxScaler(), estimator)

Which of the following is a suitable choice for warm_start if you wanted to plot learning curve with decreasing loss when trained for 100 epochs? Make necessary assumptions.

A
True
B
False
C
Yes
D
No
E
None of these

Show answer

Correct answer

A
True

Question 17 on its own page

Question 18

+3 marksOne correct option

A
It controls the number of weak learners.
B
It shrinks the contribution of each classifier.
C
It sets the weight of each weak learner.
D
It adjusts the speed at which the model learns.

Show answer

Correct answer

B
It shrinks the contribution of each classifier.

Question 18 on its own page

Question 19

+4 marksOne correct option

The following code produces an output of 0.9125. How is the output expected to change if we increase the max_depth value?:

python

1from sklearn.datasets import load_wine
2from sklearn.tree import DecisionTreeClassifier
3from sklearn.model_selection import train_test_split
4X,y = load_wine(as_frame = True, return_X_y = True)
5
6X_train,X_test,y_train,y_test = train_test_split(X,
7                                                 y,
8                                                 test_size = 0.10,
9                                                 random_state = 12)
10
11clf = DecisionTreeClassifier(max_depth = 2,
12                             min_samples_split = 2,
13                             min_samples_leaf=3,
14                             random_state = 81)
15
16clf.fit(X_train, y_train)
17print(clf.score(X_train, y_train))

A
Output score is likely to increase.
B
Output score is likely to decrease.
C
Output score may increase or decrease.
D
Output score will remain the same.

Show answer

Correct answer

A
Output score is likely to increase.

Question 19 on its own pageAlso asked in End Term 30 Apr 2023, End Term 30 Apr 2023

Question 20

+4 marksOne correct option

Consider the following code. How many different combinations of DecisionTreeClassifier models will be trained internally?

python

1from sklearn.tree import DecisionTreeClassifier
2from sklearn.model_selection import GridSearchCV
3param_grid = [{'max_depth':range(1, 10, 2),
4               'min_samples_split': range(1, 10, 3)}]
5gs = GridSearchCV(DecisionTreeClassifier(), param_grid, cv = 5)
6gs.fit(X,y)

A
20
B
75
C
8
D
15
E
40

Show answer

Correct answer

D
15

Question 20 on its own page

Question 21

+4 marksOne correct option

Consider the following code for a VotingClassifier. What is the effect of setting voting='soft'?

python

1from sklearn.ensemble import VotingClassifier
2clf1 = LogisticRegression()
3clf2 = RandomForestClassifier()
4clf3 = SVC(probability=True)
5eclf = VotingClassifier(estimators=[('lr', clf1),
6                                    ('rf', clf2),
7                                    ('svc', clf3)],
8                                    voting='soft')
9eclf.fit(X_train, y_train)

A
The final predictions are based on the majority vote.
B
The final predictions are based on the average of probabilities predicted by each classifier.
C
The final predictions are based on the weighted sum of the predictions.
D
The final predictions are based on the classifier with the highest accuracy.

Show answer

Correct answer

B
The final predictions are based on the average of probabilities predicted by each classifier.

Question 21 on its own page

Question 22

+4 marksOne correct option

A
The mean accuracy across all cross-validation folds.
B
The mean precision across all cross-validation folds.
C
The mean recall across all cross-validation folds.
D
The mean F1 score across all cross-validation folds.

Show answer

Correct answer

A
The mean accuracy across all cross-validation folds.

Question 22 on its own page

Question 23

+4 marksOne correct option

A
Plotting the sum of the squared distances of samples to their closest cluster center
B
by Changing the distance metric for KMeans
C
By initialising clustering centroids very far from each other
D
All of these

Show answer

Correct answer

A
Plotting the sum of the squared distances of samples to their closest cluster center

Question 23 on its own page

Question 24

+2 marksOne or more correct options

Consider the following code:

python

1from sklearn.datasets import load_iris
2from sklearn.model_selection import train_test_split
3X, y = load_iris(return_X_y = True)

The sizes of X and y are (150, 4) and (150,) respectively. Which of the following would be the correct code snippet to split X and y into training and test data such that test data has exactly 30 samples?

Select all that apply.

Show answer

Correct answers

Question 24 on its own page

Question 25

+2 marksOne or more correct options

Consider the following code block:

python

1from sklearn.linear_model import linear_regression
2from sklearn.model_selection import cross_val_score
3from sklearn.model_selection import ShuffleSplit
4lin_reg = linear_regression()
5shuffle_split = ShuffleSplit(n_splits=5, test_size=0.2, random_state=42)
6score = cross_val_score(lin_reg, X, y, cv=shuffle_split,
7scoring='------------------------')

Which of the following may be appropriate to be filled in the blank space?

Select all that apply.

Show answer

Correct answers

Question 25 on its own page

Question 26

+2 marksOne or more correct options

Which of the following is a hyper parameter?

Select all that apply.

Show answer

Correct answers

Question 26 on its own page

Question 27

+2 marksOne or more correct options

Which of the following models are inherently multiclass models?

Select all that apply.

A
Perceptron
B
DecisionTreeClassifier
C
KNeighborClassifier
D
LogisticRegression

Show answer

Correct answers

B
DecisionTreeClassifier
C
KNeighborClassifier

Question 27 on its own page

Question 28

+2 marksOne or more correct options

Which of the following class(es) is/are used to instantiate a neural network in Sklearn.

Select all that apply.

A
SGDClassifier()
B
MLPClassifier()
C
NNClassifier()
D
MLPRegressor()

Show answer

Correct answers

B
MLPClassifier()
D
MLPRegressor()

Question 28 on its own pageAlso asked in End Term 30 Apr 2023, End Term 30 Apr 2023

Question 29

+3 marksOne or more correct options

Which of the following is correct?

Select all that apply.

Show answer

Correct answers

Question 29 on its own page

Question 30

+3 marksOne or more correct options

Which of the following processes can be done if we have a dataset with imbalanced target class distribution?

Select all that apply.

A
Remove all the minority classes
B
Up-sample the minority classes
C
Remove all the majority classes
D
Down-sample the majority classes
E
create synthetic samples to balance the classes

Show answer

Correct answers

B
Up-sample the minority classes
D
Down-sample the majority classes
E
create synthetic samples to balance the classes

Question 30 on its own page

Question 31

+3 marksOne or more correct options

Which of the following is true about Naive Bayes algorithm ?

Select all that apply.

A
It is primarily used for regression problems
B
It is primarily used for classification problems
C
Hyperparameter tuning is required
D
Hyperparameter tuning is not required

Show answer

Correct answers

B
It is primarily used for classification problems
D
Hyperparameter tuning is not required

Question 31 on its own pageAlso asked in End Term 30 Apr 2023, End Term 30 Apr 2023

Question 32

+3 marksOne or more correct options

Which of the following is/are correct regarding RadiusNeighborsClassifier

Select all that apply.

A
Only 5 neighbours in the range of some radius are used to compute the label of a sample.
B
All the neighbours in the range of some radius are used to compute the label of a sample.
C
It is sensitive to outliers.
D
It is not sensitive to outliers.

Show answer

Correct answers

B
All the neighbours in the range of some radius are used to compute the label of a sample.
D
It is not sensitive to outliers.

Question 32 on its own page

Question 33

+3 marksOne or more correct options

Select all that apply.

A
Data is continuously being generated
B
Data is generated every month
C
Whole data is generated and its in a huge file size
D
For very small dataset

Show answer

Correct answers

A
Data is continuously being generated
B
Data is generated every month
C
Whole data is generated and its in a huge file size

Question 33 on its own page

Question 34

+4 marksOne or more correct options

Select all that apply.

A
The neural network contains 3 hidden layers with 5 neurons in each hidden layer
B
The neural network contains 5 hidden layers with 3 neurons in each hidden layer
C
The neural network contains 2 hidden layers with 3 neurons in the second hidden layer
D
The neural network contains 2 hidden layers with 5 neurons in the first hidden layer
E
All of the given options are correct

Show answer

Correct answers

C
The neural network contains 2 hidden layers with 3 neurons in the second hidden layer
D
The neural network contains 2 hidden layers with 5 neurons in the first hidden layer

Question 34 on its own page

Question 35

+5 marksOne or more correct options

Consider the following block of code:

python

1from sklearn.datasets import load_breast_cancer
2from sklearn.tree import DecisionTreeClassifier
3from sklearn.model_selection import train_test_split
4X,y = load_breast_cancer(as_frame = True, return_X_y = True)
5X_train,X_test,y_train,y_test = train_test_split(X,y,
6                                                 test_size = 0.2,
7                                                 random_state=42)
8clf = DecisionTreeClassifier(min_samples_split = 6,
9                             min_samples_leaf = 4,
10                             random_state = 5)
11clf.fit(X_train, y_train)
12print(clf.score(X_test, y_test))

In which of the following scenarios, the split will can happen at node N?

Select all that apply.

A
Number of samples at node N = 15. If it is split, it will result in 9 samples in the left child node and 6 sample in the right child node.
B
Number of samples at node N = 5. If it is split, it will result in 4 samples in the left child node and 2 samples in the right child node.
C
Number of samples at node N = 12. If it is split, it will result in 3 samples in the left child node and 9 samples in the right child node.
D
Number of samples at node N = 7. If it is split, it will result in 4 samples in the left child node and 3 samples in the right child node.

Show answer

Correct answers

A
Number of samples at node N = 15. If it is split, it will result in 9 samples in the left child node and 6 sample in the right child node.
D
Number of samples at node N = 7. If it is split, it will result in 4 samples in the left child node and 3 samples in the right child node.

Question 35 on its own page

Question 36

+2 marksNumerical answer

Show answer

Correct answer: 0.3235 (accepted within ±0.0045)

Question 36 on its own page

Question 37

+4 marksNumerical answer

Show answer

Correct answer: 180

Question 37 on its own page