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All Model Evaluation questions

Question 5 of 6 Advanced Concept

Why can choosing a threshold on the test set be misleading?

Short answer

Choosing a decision threshold on the test set uses the same observations to make a decision and to judge its success. The reported performance is then optimistically biased because the threshold was tuned to accidental patterns in that test sample.

Why

Training a model and selecting the cutoff are both forms of learning from data. The final test sample is meant to represent unseen cases and should not participate in either step. Suppose a classifier outputs calibrated or uncalibrated scores.

The team tries dozens of cutoffs on the final test set and reports the one with the highest F1. That result is no longer an independent assessment because the cutoff was chosen to maximize a noisy test statistic. Instead, decide the intended trade-off first, tune the threshold on training folds or a dedicated validation cohort and evaluate once on a held-out cohort.

For small studies, grouped cross-validation or nested procedures may be needed to prevent patient or site overlap. In deployment, the threshold should also reflect decision costs, prevalence and clinical capacity, not an arbitrary metric peak.

An example

Ten thresholds are tried on 80 test patients and one looks unusually good. Its apparent advantage may vanish on the next group of patients.

Common mistakes

  • Treating threshold selection as harmless post-processing or using the test cohort repeatedly until the numbers look acceptable