The authors released a scenario‑based guide that describes fifteen controlled experiments designed to show how different factors—like class prevalence, decision thresholds and aggregation rules—alter popular performance metrics. The guide puts each factor on a fixed, published data set and varies only one element at a time to make the effect clear.

To make sure the numbers are trustworthy, the study recomputed every metric with three separate libraries and an independent implementation. All results were cross‑checked using Monte‑Carlo and bootstrap resampling, and every check is documented in an appendix.

A key illustration comes from Scenario S6, where a threshold chosen to minimise validation cost turns out to give a higher test‑set cost. The test‑set sensitivity reaches 0.95, but a rule that explicitly sets a 0.95 sensitivity limit achieves a slightly lower cost. This example shows that optimisation on a validation split does not guarantee the best performance on new data.

The authors note that the scenarios are meant to isolate mechanics, not to estimate how often each mechanism appears in the literature. They also planned, but did not carry out, a real‑data audit of reporting practices.

Overall, the paper supplies reproducible scripts, input files and a reporting checklist that aim to improve transparency in machine‑learning evaluation, while candidly noting the limits of its illustrative design.