How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Take-home Project rounds at Pinterest.

What role is this question designed for?

This question is commonly asked for Machine Learning Engineer candidates at Pinterest during technical interviews.

Implement Naive Bayes classifier from scratch

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of probabilistic classification, specifically competence in estimating class priors, feature likelihood parameters, and computing posterior scores for Naive Bayes variants.

Feb 9, 2026, 12:00 AM

Machine Learning Engineer

Take-home Project

Machine Learning

Implement a Naive Bayes classifier from scratch (you may use NumPy).

Write a class with:

fit(X, y) : estimate class priors and feature likelihood parameters.
predict(X) : compute posteriors (or unnormalized log-posteriors) and output the most likely class.

Specify which variant you implement (e.g., Multinomial Naive Bayes for count features, Bernoulli NB for binary features, or Gaussian NB for continuous features). Your implementation should clearly compute:

prior $P(y=c)$
likelihood parameters for $P(x\mid y=c)$
posterior scoring using Bayes’ rule with numerical stability (typically log-probabilities).

Solution

Show

Comments (0)

Loading comments...

Browse More Questions

More Machine Learning•More Pinterest•More Machine Learning Engineer•Pinterest Machine Learning Engineer•Pinterest Machine Learning•Machine Learning Engineer Machine Learning