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Supervised Learning PYQs for GATE DA

GATE DA Supervised Learning: 9 chapters, 21 previous year questions (81% of Machine Learning), 0 practice questions and one solved question from each chapter.

A question from this chapter

Question 1

2026 PYQ

Consider that for a supervised learning task, the objective function being minimized is f_{w} (x) = w x, where x \in R is the input and w \in R is the parameter. Stochastic Gradient Descent with learning rate of 0.10 is used for parameter updates. Suppose that at the end of iteration i, the value of w becomes 10.00. Let x = 10.00 be the input for iteration (i + 1) . The value of w at the end of iteration (i + 1) is __________ . (Rounded off to two decimal places)

Question 2

2025 PYQ

Consider designing a linear classifier y = s i g n (f (x; w, b)), f (x; w, b) = w^{T} x + b on a dataset D = {(x_{1}, y_{1}), (x_{2}, y_{2}), ..., (x_{N}, y_{N})}, x_{i} \in R^{d}, y_{i} \in {+ 1, - 1}, i = 1, 2, ..., N . Recall that the sign function outputs + 1 if the argument is positive, and - 1 if the argument is non-positive. The parameters w and b are updated as per the following training algorithm: w_{n e w} = w_{o l d} + y_{n} x_{n}, b_{n e w} = b_{o l d} + y_{n} whenever s i g n (f (x_{n}; w_{o l d}, b_{o l d})) \neq = y_{n} . In other words, whenever the classifier wrongly predicts a sample (x_{n}, y_{n}) from the dataset, w_{o l d} gets updated to w_{n e w}, and likewise b_{o l d} gets updated to b_{n e w} . Consider the case (x_{n}, + 1), f (x_{n}; w_{o l d}, b_{o l d}) < 0 . Then

Question 3

2026 PYQ

Consider that 20 stories of Author X and 10 stories of Author Y were kept together without mentioning the names of the authors. A classifier was then asked to predict the author (X or Y) of each of these stories. Let, out of X’s stories, 6 were classified as that of Y. On the other hand, out of Y’s stories, 2 were classified as that of X. Considering X and Y as two classes, which of the following statements is/are true?

Question 4

2026 PYQ

For a classification problem, Principal Component Analysis (PCA) has been used to reduce the dimensionality of a feature space from 100 to 10. Which of the following options is true about the angle θ between the first and the tenth principal components?

Question 5

2024 PYQ

Details of ten international cricket games between two teams “Green” and “Blue” are given in Table C. This table consists of matches played on different pitches, across formats along with their winners. The attribute Pitch can take one of two values: spin-friendly (represented as S) or pace-friendly (represented as F). The attribute Format can take one of two values: one-day match (represented as O) or test match (represented as T . A cricket organization would like to use the information given in Table C to develop a decision-tree model to predict outcomes of future games between these two teams. To develop such a model, the computed InformationGain(C, Pitch) with respect to the Target is ______ (rounded off to two decimal places). Match Number Pitch Format Winner (Target) 1 S T Green 2 S T Blue 3 F O Blue 4 S O Blue 5 F T Green 6 F O Blue 7 S O Green 8 F T Blue 9 F O Blue 10 S O Green

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Supervised Learning PYQs for GATE DA

GATE DA Supervised Learning: 9 chapters, 21 previous year questions (81% of Machine Learning), 0 practice questions and one solved question from each chapter.

About Supervised Learning Previous Year Questions (PYQs)

21 previous year questions from Supervised Learning in GATE DA, grouped by chapter with the exam year, answer key and step-by-step solution for each.

Supervised Learning Weightage in GATE DA

Supervised Learning accounts for 21 of 26 Machine Learning previous year questions in our bank (81%), about 7 per paper across 3 papers.

Supervised Learning Chapter Matrix

Chapter	Topics	PYQs	Share of unit PYQs
Chapter 1 — Supervised Learning		0	0%
Regression, Regularization and Gradient-Based Learning	Ridge Regression and Regularization, Ridge Regression and Regularization, Gradient-Based Parameter Updates, Gradient-Based Parameter Updates, Linear Least-Squares Regression, Linear Least-Squares Regression	4	19%
Chapter 2 — Supervised Learning		0	0%
Linear Classifiers, Discriminant Analysis and Margin-Based Methods	Fisher Discriminant and Distance-Based Linear Classifiers, Fisher Discriminant and Distance-Based Linear Classifiers, Linear Separability and Perceptron-Style Updates, Linear Separability and Perceptron-Style Updates, Support Vector Machines, Margins and Support Vectors, Support Vector Machines, Margins and Support Vectors	6	29%
Chapter 3 — Supervised Learning		0	0%
Model Selection, Cross-Validation and Performance Metrics	Cross-Validation and Model Selection, Cross-Validation and Model Selection, Classification Performance Metrics, Classification Performance Metrics	2	10%
Dimensionality Reduction and Model Families	Principal Component Analysis and Orthogonal Components, Principal Component Analysis and Orthogonal Components, Generative, Discriminative and Dimensionality Reduction Models, Generative, Discriminative and Dimensionality Reduction Models	2	10%
Decision Trees, Naive Bayes and k-Nearest Neighbors	k-Nearest Neighbors Classification, k-Nearest Neighbors Classification, Naive Bayes Parameter Estimation, Naive Bayes Parameter Estimation, Decision Trees and Attribute-Based Classification, Decision Trees and Attribute-Based Classification	3	14%
Neural Networks and Activation Functions	ReLU Activation Properties and Gradients, ReLU Activation Properties and Gradients, Neural Network Architecture, Parameters and Equivalence, Neural Network Architecture, Parameters and Equivalence	4	19%

One Solved Question from Each Supervised Learning Chapter

Question 1 · Regression, Regularization and Gradient-Based Learning · 2026 NAT

Consider that for a supervised learning task, the objective function being minimized is

f_{w} (x) = w x

, where

x \in R

is the input and

w \in R

is the parameter. Stochastic Gradient Descent with learning rate of 0.10 is used for parameter updates.

Suppose that at the end of iteration

i

, the value of

w

becomes 10.00.

Let

x = 10.00

be the input for iteration

(i + 1)

.

The value of

w

at the end of iteration

(i + 1)

is __________ . (Rounded off to two decimal places)

Question 2 · Linear Classifiers, Discriminant Analysis and Margin-Based Methods · 2025 MCQ

Consider designing a linear classifier

y = s i g n (f (x; w, b)), f (x; w, b) = w^{T} x + b

on a dataset

D = {(x_{1}, y_{1}), (x_{2}, y_{2}), ..., (x_{N}, y_{N})}

x_{i} \in R^{d}

y_{i} \in {+ 1, - 1}

i = 1, 2, ..., N

. Recall that the sign function outputs

+ 1

if the argument is positive, and

- 1

if the argument is non-positive. The parameters

w

and

b

are updated as per the following training algorithm:

w_{n e w} = w_{o l d} + y_{n} x_{n}, b_{n e w} = b_{o l d} + y_{n}

whenever

s i g n (f (x_{n}; w_{o l d}, b_{o l d})) \neq = y_{n}

. In other words, whenever the classifier wrongly predicts a sample

(x_{n}, y_{n})

from the dataset,

w_{o l d}

gets updated to

w_{n e w}

, and likewise

b_{o l d}

gets updated to

b_{n e w}

. Consider the case

(x_{n}, + 1)

f (x_{n}; w_{o l d}, b_{o l d}) < 0

. Then

A.
$f (x_{n}; w_{n e w}, b_{n e w}) > f (x_{n}; w_{o l d}, b_{o l d})$
B.
$f (x_{n}; w_{n e w}, b_{n e w}) < f (x_{n}; w_{o l d}, b_{o l d})$
C.
$f (x_{n}; w_{n e w}, b_{n e w}) = f (x_{n}; w_{o l d}, b_{o l d})$
D.
$y_{n} f (x_{n}; w_{o l d}, b_{o l d}) > 1$

Question 3 · Model Selection, Cross-Validation and Performance Metrics · 2026 MSQ

Consider that 20 stories of Author X and 10 stories of Author Y were kept together without mentioning the names of the authors. A classifier was then asked to predict the author (X or Y) of each of these stories. Let, out of X’s stories, 6 were classified as that of Y. On the other hand, out of Y’s stories, 2 were classified as that of X.

Considering X and Y as two classes, which of the following statements is/are true?

A.
Accuracy of the classifier is 11/15.
B.
Precision of Class X is higher than the Precision of Class Y.
C.
Recall of Class X is higher than the Recall of Class Y.
D.
Accuracy of the classifier is 14/15.

Question 4 · Dimensionality Reduction and Model Families · 2026 MCQ

For a classification problem, Principal Component Analysis (PCA) has been used to reduce the dimensionality of a feature space from 100 to 10.
Which of the following options is true about the angle

θ

between the first and the tenth principal components?

A.
$θ = 0^{\circ}$
B.
$θ = 9 0^{\circ}$
C.
$9 0^{\circ} < θ \leq 18 0^{\circ}$
D.
$0 < θ < 9 0^{\circ}$

Question 5 · Decision Trees, Naive Bayes and k-Nearest Neighbors · 2024 NAT

Details of ten international cricket games between two teams “Green” and “Blue”
are given in Table C. This table consists of matches played on different pitches,
across formats along with their winners. The attribute Pitch can take one of two
values: spin-friendly (represented as

S

) or pace-friendly (represented as

F

). The
attribute Format can take one of two values: one-day match (represented as

O

) or
test match (represented as

T

.
A cricket organization would like to use the information given in Table C to develop
a decision-tree model to predict outcomes of future games between these two teams.
To develop such a model, the computed InformationGain(C, Pitch) with respect to
the Target is ______ (rounded off to two decimal places).

Match Number	Pitch	Format	Winner (Target)
1	$S$	$T$	Green
2	$S$	$T$	Blue
3	$F$	$O$	Blue
4	$S$	$O$	Blue
5	$F$	$T$	Green
6	$F$	$O$	Blue
7	$S$	$O$	Green
8	$F$	$T$	Blue
9	$F$	$O$	Blue
10	$S$	$O$	Green

A question from this chapter

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Honest progress

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Supervised Learning PYQs for GATE DA

About Supervised Learning Previous Year Questions (PYQs)

Supervised Learning Weightage in GATE DA

Supervised Learning Chapter Matrix

More from Machine Learning

One Solved Question from Each Supervised Learning Chapter