Debug threshold outside 0..1 in the threshold prediction game

Do not start with a speculative fix for the threshold prediction game. Force the failure on purpose, save the before output, then change one cause at a time. Retries are allowed only for transient conditions—not for bad input that will fail forever on prediction-game.

try:
    thr=1.5
    if not 0<=thr<=1: raise ValueError('threshold out of range')
except ValueError as e:
    print(e)

Expected evidence: threshold out of range. If you cannot reproduce on demand, you do not yet control the failure mode for prediction-game.

After repair, rerun the exact reproduction command. Keep the failing fixture as a regression seed for the observability page. For the threshold tuner on five labeled scores, remember the claim you are restoring: for a chosen threshold, accuracy and confusion counts are exact on the fixture.

Store a one-command reproduction for: threshold outside 0..1, or reporting accuracy without TP/FP/TN/FN. The command should use 5 (truth, score) pairs or a minimal mutant of it. Paste the failing output into notes/failure-before.txt (or your shell scrollback as copied text). After the fix, paste notes/failure-after.txt and keep both.

Retries belong only on transient faults. If the failure is bad input, a bad allowlist, or a logic bug in the threshold prediction game, retrying will amplify cost without repairing trust around choose a cutoff that balances errors without claiming generalization from five rows.

Classify the failure as prevent, detect, contain, or recover—using this lab’s language, not a generic poster. For prediction-game, the first fix should usually be detect+prevent at the boundary, because threshold outside 0..1, or reporting accuracy without TP/FP/TN/FN is cheaper to stop early than to explain in production prose.

At the debugging 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 before/after evidence for the characteristic failure 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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