Classifying reviewers as lazy or careful with limited labels

Propose a classification rule based on P(lazy | data) > 0.5 using Bayes’ theorem. 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?

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

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