Define the threshold prediction game input contract

The accepted input remains: binary truths, scores in 0..1, and a threshold. Keep parsing and normalization in functions that do not score, train, or call a model. That split lets a test fail the boundary without blaming the core logic. The user-facing decision stays: choose a cutoff that balances errors without claiming generalization from five rows.

def check(score, thr):
    if not 0<=score<=1: raise ValueError('score')
    if not 0<=thr<=1: raise ValueError('threshold')
print(check(0.55,0.5) or 'ok')

Expected evidence: ok. If the contract is silent on a bad value, later debugging will look like an algorithm bug when it is really a data bug.

Write one assertion for a neighboring valid input to the threshold prediction game so tightening the boundary does not over-reject. Document field names and types the way a teammate would need them on day two of prediction-game—not as comments you plan to delete.

List every field in 5 (truth, score) pairs and mark each as required, optional, or forbidden. Required fields must fail loudly when missing; optional fields need defaults you can quote in a test; forbidden fields (secrets, raw PII, path escapes) must never be accepted silently. This list is the contract for the threshold prediction game.

Add one sentence about encoding, units, or timezones if relevant to binary truths, scores in 0..1, and a threshold. Contracts that ignore units create “correct” programs that still ship wrong decisions when someone tries to choose a cutoff that balances errors without claiming generalization from five rows.

Write the error string you want for the most likely bad input. Prefer ValueError('threshold out of range')-style messages over generic invalid input. The contract’s job is to make threshold outside 0..1, or reporting accuracy without TP/FP/TN/FN harder to confuse with a model or algorithm bug later.

At the data contract stage for prediction-game, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about 5 (truth, score) pairs that later pages inherit without redefining success. Keep that fixture small enough to inspect by hand, keep outputs copy-pasteable as text, and refuse to narrate this baseline as if it were a production SLA: confusion counts computed by hand at threshold 0.5.

For this page specifically, success looks like malformed inputs rejected with field-named errors while still centering the user decision to choose a cutoff that balances errors without claiming generalization from five rows. If you cannot point to a file, command, or assertion that proves that for the threshold prediction game, stay on this page instead of advancing.

Confusion matrix glossary

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