A binary classifier is a model that assigns each case to one of two categories, such as “trial succeeds” versus “trial fails.” This simple framing hides complexities like class imbalance, threshold selection and differing costs of false positives and false negatives, which are crucial when AI tools influence decisions about continuing or cancelling expensive, patient‑involving clinical programs.