Instrument the threshold prediction game

Instrument the threshold tuner on five labeled scores so a run records enough structure to debug offline: counts, latency if relevant, pass/fail of confusion matrix sums to 5, and a stable stage name. Redact secrets and raw credentials from every event.

import json
print(json.dumps({'threshold':0.5,'acc':0.8,'n':5,'split':'fixture-only'}))

Expected evidence: metrics with fixture-only label. Prefer JSON or structured text you can grep in CI over prose logs for prediction-game.

Turn one historical failure—especially threshold outside 0..1—into a test that fails if the signal disappears for the threshold prediction game. Observability without a failing test is optional decoration; observability with a test is part of the prediction-game artifact.

Draft a three-field event for the threshold prediction game: stage, ok, and one domain field derived from confusion matrix sums to 5; accuracy matches hand count. Add fixture_id or docs_version when content can change. Explicitly list fields that must never appear (tokens, passwords, raw prompts) because treating the five-row accuracy as a production SLA is in scope for this lab.

Wire one assertion that fails if the threshold prediction game event is missing after a run. Observability that cannot fail a test will not survive contact with a busy prediction-game repository.

Imagine a teammate opens only your event stream after a bad deploy. Could they tell whether 5 (truth, score) pairs was wrong, whether threshold outside 0..1, or reporting accuracy without TP/FP/TN/FN returned, or whether treating the five-row accuracy as a production SLA slipped through? If not, rename fields until those three stories are distinguishable.

At the testing and observability 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 a structured event schema locked by a test 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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