Question

A data scientist has trained a binary classifier that predicts whether a piece of manufacturing equipment will fail within 24 hours. The model outputs a probability score, and the current default threshold of 0.5 is used to convert scores to labels. A missed failure (false negative) causes an expensive unplanned shutdown, while a false alarm (false positive) only requires a cheap manual inspection. Business stakeholders want to minimize costly shutdowns without deploying a completely different model. What is the MOST appropriate action the data scientist should take?

Accepted Answer

Lower the classification threshold below 0.5 so more instances are predicted as failures, increasing recall for the failure class.

Answer

Raise the classification threshold above 0.5 so only high-confidence predictions are labeled as failures, increasing precision.

Answer

Retrain the model using accuracy as the objective metric to improve overall correctness.

Answer

Keep the 0.5 threshold because it is the mathematically optimal cutoff for any probabilistic classifier.