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This is a medium difficulty Machine Learning question, commonly asked during Onsite rounds at Meta.

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This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Classify Reviewers Using Bayesian Probability for Accuracy Analysis

Last updated: Mar 29, 2026

Quick Overview

This question evaluates Bayesian inference and statistical decision-making for latent class classification, specifically using posterior probabilities to distinguish reviewer types and derive false-positive (Type I) and false-negative (Type II) rates.

Scenario

Classifying reviewers as lazy or careful with limited labels

Context (completed)

You are auditing a pool of reviewers who can be either:

Lazy (L): lower accuracy
Careful (C): higher accuracy

Assume a known prior mixture π = P(L) and per-review accuracies a_L and a_C with a_C > a_L. For each reviewer, you observe their performance on n gold items (with known ground truth), yielding k correct out of n.

Question

Use Bayes' theorem to propose the classification rule that predicts "lazy" when P(L | data) > 0.5.
Given the true mixture and review accuracies, derive the false-positive and false-negative rates of this rule.
If every reviewer is required to write the same large number of reviews (e.g., 100), how will Type I and Type II error rates change?

Hints: Treat reviewer type as the latent class and use a Bayesian optimal decision boundary; error rates shrink as review count grows.

Solution

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