GATE DA 2025 Set 1 — Question 35

NAT+1 / -0MediumNaive Bayes ClassifierClassificationMachine Learning

Machine Learning → Classification → Naive Bayes Classifier

Last updated 5 September 2026

Question

y_1, y_2

P(y_1|x) = \frac{P(x|y_1)P(y_1)}{P(x|y_1)P(y_1) + P(x|y_2)P(y_2)} = \frac{(3/4)(1/3)}{(3/4)(1/3) + (1/4)(2/3)} = \frac{1/4}{1/4 + 2/12} = \frac{1/4}{1/4 + 1/6} = \frac{3/12}{3/12 + 2/12} = \frac{3}{5} = 0.6

Review answer and solution without JavaScript

Interactive answer checking needs JavaScript. The published solution is available below.

P(y_1|x) = \frac{P(x|y_1)P(y_1)}{P(x|y_1)P(y_1) + P(x|y_2)P(y_2)} = \frac{(3/4)(1/3)}{(3/4)(1/3) + (1/4)(2/3)} = \frac{1/4}{1/4 + 2/12} = \frac{1/4}{1/4 + 1/6} = \frac{3/12}{3/12 + 2/12} = \frac{3}{5} = 0.6

Continue learning with Success Tracker

Use Success Tracker to ask about the reasoning, then try another GATE DA question to check your understanding.

AI-powered practice· Unlimited practice on eligible plans

PYQs with solutions: Attempt available previous-year questions, then compare your reasoning with the worked solution. Coverage varies by stream.
Practice that adapts: Choose a topic, work on weaker areas and bookmark questions to revisit. Your attempts feed your progress tracking.
AI doubt support: Ask follow-up questions about a step or concept while practising, instead of stopping at the final answer.

Unlimited practice is available on eligible plans. Free practice and AI usage have limits; check the current plan allowances before choosing.

This page stays readable without an account. AI responses can be wrong; check them against the solution and source material.