Mastery: ship checklist

Shipping means handing off evidence, not only source code. Package the evaluator with boundary tests and a short metric note. Downstream users need the threshold, score range, positive-class meaning, and date of the evaluation data. Rebuild or rerun from the documented starting point. If another person needs an undocumented fact from your machine, the handoff is incomplete.

Keep the example small enough to inspect manually. Small does not mean careless: boundary values, file locations, feature order, and held-out data still determine whether the result means what you claim.

truth = [1, 0, 1, 1, 0]
scores = [0.8, 0.3, 0.6, 0.9, 0.2]
threshold = 0.5
preds = [int(score >= threshold) for score in scores]

pairs = list(zip(truth, preds))
tp = pairs.count((1, 1)); fp = pairs.count((0, 1))
tn = pairs.count((0, 0)); fn = pairs.count((1, 0))
accuracy = (tp + tn) / len(pairs)
print(preds, f"accuracy={accuracy:.2f}", tp, fp, tn, fn)

Expected evidence:

[1, 0, 1, 1, 0] accuracy=1.00 3 0 2 0

The output may include version-specific details such as hashes, paths, fitted thresholds, or final decimal places. Compare the structural facts described here rather than copying placeholders. If the structure differs, stop and inspect the earliest unexpected line.

1. the comparison returns Boolean values and int converts them to zero or one.
2. zip aligns each known label with exactly one prediction.
3. the four tuple counts partition every example into confusion-matrix cells.
4. accuracy divides correct cells by all evaluated pairs; formatting shows two decimal places.

These lines form one chain: binary truth labels, scores between zero and one, and a chosen threshold becomes binary predictions plus accuracy, true positives, false positives, true negatives, and false negatives. Change only one input first. When several values change together, you cannot tell which change caused the new behavior.

• First failure: zip silently stops at the shorter list, so assert equal lengths before scoring. Re-run the smallest command that proves the repair.
• Second failure: an empty list causes division by zero and is not a meaningful evaluation. Preserve the failing input as a test when it represents a realistic mistake.
• Misleading success: choosing a threshold after repeatedly inspecting the same labels overfits the evaluation set even though no model weights changed. A clean-looking final line cannot cancel contradictory intermediate evidence.

When debugging, copy the exact error text and inspect names, paths, shapes, types, and versions. Explain the cause in one sentence before changing code. That discipline prevents a guessed repair from creating a second defect.

The deliverable for this step is a threshold evaluator that reports predictions, accuracy, and the confusion counts behind that accuracy.

For the current ship step, save the smallest useful evidence: the relevant command, its output, and the input that produced it. Do not use a screenshot as the only record when text can be copied and searched. Keep generated artifacts separate from source inputs so rerunning the example does not destroy the evidence it is meant to evaluate.

Document who is affected by an automatic positive label and provide a human review path for consequential decisions. Never describe a toy score as calibrated probability without evidence.

Return to your opening prediction. Mark it correct or rewrite it with the condition you missed. Then explain the difference between a successful execution and a trustworthy result for this specific example.