Decision Trees, Naive Bayes and k-Nearest Neighbors Previous Year Questions (PYQs) for GATE DA: 3+ Solved Questions with Step-by-Step Solutions

Solve 3+ Decision Trees, Naive Bayes and k-Nearest Neighbors previous year questions for GATE DA with answers and detailed solutions. Free sample questions below.

k-Nearest Neighbors Classification

"Tell me who your neighbors are, and I will tell you who you are."

Supervised Learning → Decision Trees, Naive Bayes & k-NN

What you will learn here

The core intuition behind instance-based, lazy learning.
Step-by-step distance calculation and majority voting.
Critical exam traps: choice of k, feature scaling, and decision boundaries.

The Core Intuition: Lazy Learning

k-Nearest Neighbors (k-NN) is an instance-based or lazy learning algorithm.

No explicit training phase: The algorithm does not learn a generalized function or estimate parameters. It merely stores the entire training dataset in memory.
Prediction is the real work: All computation is deferred until a prediction is required for a new, unseen query point.
The fundamental assumption: Data points that are close to each other in the feature space are likely to belong to the same class.

Decision Trees, Naive Bayes and k-Nearest Neighbors: Solved Questions with Step-by-Step Explanations (3 Problems)

Question 1 · Machine Learning NAT

Given the two-dimensional dataset consisting of 5 data points from two classes
(circles and squares) and assume that the Euclidean distance is used to measure the
distance between two points. The minimum odd value of

k

k

-nearest neighbor
algorithm for which the diamond (⋄) shaped data point is assigned the label square
is ______.

Question 2 · Machine Learning MCQ

Given a dataset with

K

binary-valued attributes (where

K > 2

) for a two-class
classification task, the number of parameters to be estimated for learning a naïve
Bayes classifier is

A.
$2^{K} + 1$
B.
$2 K + 1$
C.
$2^{K + 1} + 1$
D.
$K^{2} + 1$

Question 3 · Machine Learning 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

Decision Trees, Naive Bayes and k-Nearest Neighbors Previous Year Questions (PYQs) for GATE DA: 3+ Solved Questions with Step-by-Step Solutions

k-Nearest Neighbors Classification

k-Nearest Neighbors Classification

What you will learn here

The Core Intuition: Lazy Learning

The Core Intuition: Lazy Learning

Decision Trees, Naive Bayes and k-Nearest Neighbors: Solved Questions with Step-by-Step Explanations (3 Problems)

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