Set release boundaries for the tiny linear model

Operational risks for the hours→score linear fit with holdout center on publishing per-learner residuals that re-identify students, plus the earlier failure mode (training on the test row, or reporting R² without the holdout error). Safety lives in executable gates, allowlists, redaction, and a named owner—not in a warning paragraph under an unsafe function.

residuals={'learner_id_hash_only':True,'raw_names_exported':False}
print(residuals)

Expected evidence: no raw names exported. A failed assertion means stop, investigate, and do not publish the tiny linear model.

Name who can disable the feature, what data is retained, and how to roll back to the last known good artifact. Pin the reviewed configuration (versions, thresholds, allowlists) so “what shipped” is reconstructable for train-a-tiny-model.

Write the release blocker as a predicate, not a feeling: “Do not ship the tiny linear model if publishing per-learner residuals that re-identify students.” Pair it with a passing control that shows the reviewed configuration still works for small hours/score table with one held-out pair. Name an owner and a rollback handle (git tag, docs_version, previous image).

Security pages must not paste the happy-path demo. If your gate code looks like the implementation page, replace it with a deny/allow check aimed at publishing per-learner residuals that re-identify students.

State the data retention rule in one line (what is stored, for how long, who can read it). Then state the kill switch (env flag, config pin, or feature owner). The tiny linear model is not shippable without both, even when holdout absolute error; slope sign matches the hours/score trend looks healthy.

At the safety and operations stage for train-a-tiny-model, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about small hours/score table with one held-out pair 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: predict mean train score for the holdout before fitting.

For this page specifically, success looks like an executable deny gate for the lab-specific threat while still centering the user decision to estimate slope/intercept from study hours and score a held-out row honestly. If you cannot point to a file, command, or assertion that proves that for the tiny linear model, stay on this page instead of advancing.

Overfitting glossary

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