Determine Posterior Probability of Bad User Prediction

Q: Determine Posterior Probability of Bad User Prediction

This question evaluates a candidate's understanding of Bayesian reasoning and conditional probability, focusing on concepts such as prevalence, sensitivity, specificity, and posterior probability in classifier evaluation within the Statistics & Math domain.

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Question

Evaluating a Classifier That Flags Bad Actors

Question

You are evaluating a binary classifier for detecting bad actors among users.

Given:

Prevalence: 5% of users are truly bad.
Model performance: 95% sensitivity (true-positive rate) and 95% specificity (true-negative rate).

If the model predicts a user is bad, what is the posterior probability that the user is truly bad?

Determine Posterior Probability of Bad User Prediction

Evaluating a Classifier That Flags Bad Actors

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