Consider the following code for Ridge Regression: from sklearn.linear_model import Ridge import numpy as np X = np.array([[1, 2], [2, 4], [3, 6], [4, 8]]) y = np.array([1, 2, 3, 4]) model = Ridge(alpha=10) model.fit(X, y) coefficients = model.coef_ print(np.round(coefficients,2)) What will be the output for the coefficients?

(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

Given the following code for Polynomial Regression: from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression import numpy as np X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1) y = np.array([1, 4, 9, 16, 25]) poly = PolynomialFeatures(degree=2, interaction_only=False) X_poly = poly.fit_transform(X) model = LinearRegression() model.fit(X_poly, y) y_pred = model.predict(X_poly) print(y_pred) What will be the output of the code?

(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 implementation that utilizes sklearn to set up a machine learning model with preprocessing: 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) Which of the following statements correctly describes the effect of setting scaler_with_mean=False?

(A) The data will be standardized without centering, preserving the mean of the dataset.

(B) The data will be centered but not scaled, leading to variance issues.

(C) The parameter has no effect; the data will always be centered regardless of this setting.

(D) The scaling operation will be disabled entirely, leaving the data unchanged.

Figure from the original question paper

(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

Figure from the original question paper

(D) Support Vector Machine (SVM)

You are working on a classification project using the Wine dataset. After fitting a Gaussian Naive Bayes model, you want to assess the probability of each class for the test samples. from sklearn.datasets import load_wine from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB wine = load_wine() X = wine.data y = wine.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.3, random_state=42) gnb = GaussianNB() gnb.fit(X_train, y_train) Which of the following methods will correctly return the class probabilities for the test samples after fitting the Gaussian Naive Bayes model?

(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

Figure from the original question paper

(A) The boundaries become more complex and tightly fitted to the training data.

(B) The boundaries become more regular and smooth.

(C) Decision boundaries are unaffected by C.

(D) The number of support vectors decreases.

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

(A) It defines the tolerance for stopping criteria.

(B) It defines the learning rate schedule.

(C) It controls regularization strength.

(D) It controls the number of iterations.

Consider the following RidgeClassifier implementation in Python: from sklearn.pipeline import make_pipeline from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import RidgeClassifier X = [[1, 2], [2, 3], [3, 4]] y = [0, 1, 0] pipeline = make_pipeline(MinMaxScaler(), RidgeClassifier(alpha=1.0, fit_intercept=True)) pipeline.fit(X, y) Which of the following statements are correct? (Select all that apply)

(A) Option figure from the original question paper

(C) Option figure from the original question paper

(D) 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

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

September 2024 term · Machine Learning Practice · BSCS2008

Machine Learning Practice Quiz 2: 1 December 2024 (September 2024 term)

The IIT Madras BS Machine Learning Practice (MLP) Quiz 2 paper sat on 1 Dec 2024, in the September 2024 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: 13
MSQ: 4
Numerical: 4

Take as mock test Practise with answers

Updated 25 Sept 2026

Official paper: IIT M DIPLOMA AN EXAM QDD2 01 Dec 2024 · No negative marking.

Question 1

+3 marksOne correct option

Consider the following code for Ridge Regression:

python

1from sklearn.linear_model import Ridge
2import numpy as np
3
4X = np.array([[1, 2], [2, 4], [3, 6], [4, 8]])
5y = np.array([1, 2, 3, 4])
6
7model = Ridge(alpha=10)
8model.fit(X, y)
9coefficients = model.coef_
10print(np.round(coefficients,2))

What will be the output for the coefficients?

Show answer

Correct answer

Question 1 on its own page

Question 2

+3 marksOne correct option

Consider the following code for Stochastic Gradient Descent (SGD) Classifier:

python

1from sklearn.linear_model import SGDClassifier
2from sklearn.datasets import make_classification
3
4X_train, y_train = make_classification(n_samples=1000, n_features=20,
5                                       n_classes=2, random_state=42)
6
7model = SGDClassifier(penalty='elasticnet', l1_ratio=0.5, alpha=0.01)
8model.fit(X_train, y_train)

What is the significance of $l1\_ratio = 0.5$ in this context?

A
It assigns equal weightage to L1 and L2 regularizations.
B
It applies only L2 regularization.
C
It applies only L1 regularization.
D
It disables regularization completely.

Show answer

Correct answer

A
It assigns equal weightage to L1 and L2 regularizations.

Question 2 on its own page

Question 3

+3 marksOne correct option

Given the following code for Polynomial Regression:

python

1from sklearn.preprocessing import PolynomialFeatures
2from sklearn.linear_model import LinearRegression
3import numpy as np
4
5X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
6y = np.array([1, 4, 9, 16, 25])
7
8poly = PolynomialFeatures(degree=2, interaction_only=False)
9X_poly = poly.fit_transform(X)
10model = LinearRegression()
11model.fit(X_poly, y)
12
13y_pred = model.predict(X_poly)
14print(y_pred)

What will be the output of the code?

Show answer

Correct answer

Question 3 on its own page

Question 4

+3 marksOne correct option

Consider the following code implementation that utilizes sklearn to set up a machine learning model with preprocessing:

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)

Which of the following statements correctly describes the effect of setting scaler_with_mean=False?

A
The data will be standardized without centering, preserving the mean of the dataset.
B
The data will be centered but not scaled, leading to variance issues.
C
The parameter has no effect; the data will always be centered regardless of this setting.
D
The scaling operation will be disabled entirely, leaving the data unchanged.

Show answer

Correct answer

A
The data will be standardized without centering, preserving the mean of the dataset.

Question 4 on its own page

Question 5

+2 marksOne correct option

Show answer

Correct answer

Question 5 on its own page

Question 6

+2 marksOne correct option

Consider the following code:

python

1from sklearn.datasets import make_classification
2from sklearn.linear_model import Perceptron
3
4
5X_train, y_train = make_classification(n_samples=1000, n_features=5,
6   n_classes=2, random_state=42)
7
8clf = Perceptron(max_iter=50, warm_start=True, random_state=42)
9clf.fit(X_train, y_train)

What will be the behavior of the code?

Show answer

Correct answer

Question 6 on its own page

Question 7

+2 marksOne correct option

A
0.75
B
0.80
C
0.50
D
0.67

Show answer

Correct answer

D
0.67

Question 7 on its own page

Question 8

+2 marksOne correct option

A
LogisticRegression
B
Perceptron
C
RidgeClassifier
D
Support Vector Machine (SVM)
E
LassoClassifer

Show answer

Correct answer

C
RidgeClassifier

Question 8 on its own page

Question 9

+2 marksOne correct option

You are working on a classification project using the Wine dataset. After fitting a Gaussian Naive Bayes model, you want to assess the probability of each class for the test samples.

python

1from sklearn.datasets import load_wine
2from sklearn.model_selection import train_test_split
3from sklearn.naive_bayes import GaussianNB
4wine = load_wine()
5X = wine.data
6y = wine.target
7X_train, X_test, y_train, y_test = train_test_split(X, y, test_size
8   =0.3, random_state=42)
9
10gnb = GaussianNB()
11gnb.fit(X_train, y_train)

Which of the following methods will correctly return the class probabilities for the test samples after fitting the Gaussian Naive Bayes model?

Show answer

Correct answer

Question 9 on its own page

Question 10

+2 marksOne correct option

A
The boundaries become more complex and tightly fitted to the training data.
B
The boundaries become more regular and smooth.
C
Decision boundaries are unaffected by C.
D
The number of support vectors decreases.

Show answer

Correct answer

A
The boundaries become more complex and tightly fitted to the training data.

Question 10 on its own page

Question 11

+2 marksOne correct option

A
0.99
B
0.87
C
0.95
D
0.80

Show answer

Correct answer

C
0.95

Question 11 on its own page

Question 12

+2 marksOne correct option

Show answer

Correct answer

Question 12 on its own page

Question 13

+2 marksOne correct option

A
It defines the tolerance for stopping criteria.
B
It defines the learning rate schedule.
C
It controls regularization strength.
D
It controls the number of iterations.

Show answer

Correct answer

A
It defines the tolerance for stopping criteria.

Question 13 on its own page

Question 14

+2 marksOne or more correct options

Select all that apply.

A
’linear’
B
’quadratic’
C
’rbf’
D
’sigmoid’

Show answer

Correct answers

A
’linear’
C
’rbf’
D
’sigmoid’

Question 14 on its own page

Question 15

+2 marksOne or more correct options

You are a data scientist working on a binary classification problem to predict whether customers will buy a product based on various features such as age, income, and browsing history. You decide to use LogisticRegression from scikit-learn for this task. After some experimentation, you notice that the model is overfitting the training data, resulting in poor performance on the validation set.

Which of the following actions should you take to mitigate overfitting in your Logistic Regression model?

python

1from sklearn.linear_model import LogisticRegression
2model = LogisticRegression(penalty='...', C=...)

Select all that apply.

Show answer

Correct answers

Question 15 on its own page

Question 16

+2 marksOne or more correct options

Consider the following RidgeClassifier implementation in Python:

python

1from sklearn.pipeline import make_pipeline
2from sklearn.preprocessing import MinMaxScaler
3from sklearn.linear_model import RidgeClassifier
4
5X = [[1, 2], [2, 3], [3, 4]]
6y = [0, 1, 0]
7
8pipeline = make_pipeline(MinMaxScaler(), RidgeClassifier(alpha=1.0,
9   fit_intercept=True))
10pipeline.fit(X, y)

Which of the following statements are correct? (Select all that apply)

Select all that apply.

Show answer

Correct answers

Question 16 on its own page

Question 17

+3 marksOne or more correct options

Select all that apply.

Show answer

Correct answers

Question 17 on its own page

Question 18

+3 marksNumerical answer

Show answer

Correct answer: 0.017 (accepted within ±0.005)

Question 18 on its own page

Question 19

+3 marksNumerical answer

What will be the output of the following code?

python

1import numpy as np
2from sklearn.neighbors import KNeighborsRegressor
3
4X_train = np.array([[1, 100], [4, 400], [5, 500], [6, 600], [8, 800],
5                    [9, 900], [11, 1100], [12, 1200], [15, 1500],
6                    [18, 1800], [19, 1900]])
7
8y_train = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110])
9
10X_test = np.array([[2, 200]])
11
12knn = KNeighborsRegressor(n_neighbors=len(y_train),
13                          metric="euclidean",
14                          weights='uniform')
15knn.fit(X_train, y_train)
16print(knn.predict(X_test))

Show answer

Correct answer: 60

Question 19 on its own page

Question 20

+3 marksNumerical answer

In the following code snippet, how many bigrams will be generated when using CountVectorizer with ngram_range=(2, 2)?

python

1from sklearn.feature_extraction.text import CountVectorizer
2
3documents = [
4    "The quick brown fox",
5    "jumps over the lazy dog"
6]
7
8vectorizer = CountVectorizer(ngram_range=(2, 2))
9X = vectorizer.fit_transform(documents)
10bigrams = vectorizer.vocabulary_
11
12print(f"Number of bigrams: {len(bigrams)}")

Enter your answer (number of bigrams):

Show answer

Correct answer: 7

Question 20 on its own page

Question 21

+2 marksNumerical answer

Show answer

Correct answer: 3.5

Question 21 on its own page