Set release boundaries for the readiness random forest

Operational risks for the readiness random forest with OOB-style evidence center on shipping feature importances as causal claims about learners, plus the earlier failure mode (n_estimators=1 disguised as a forest, or leaking the test row into every tree). Safety lives in executable gates, allowlists, redaction, and a named owner—not in a warning paragraph under an unsafe function.

print({'feature_importance_is_causal':False,'policy':'report importances as associative only'})

Expected evidence: feature_importance_is_causal False. A failed assertion means stop, investigate, and do not publish the readiness random forest.

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 random-forests.

Write the release blocker as a predicate, not a feeling: “Do not ship the readiness random forest if shipping feature importances as causal claims about learners.” Pair it with a passing control that shows the reviewed configuration still works for tabular readiness set with one intentionally noisy row. 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 shipping feature importances as causal claims about learners.

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 readiness random forest is not shippable without both, even when prediction probability in (0,1); accuracy from rows not used by individual trees looks healthy.

At the safety and operations stage for random-forests, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about tabular readiness set with one intentionally noisy row 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: single decision-tree accuracy on the same split.

For this page specifically, success looks like an executable deny gate for the lab-specific threat while still centering the user decision to stabilize predictions when one noisy training row flips. If you cannot point to a file, command, or assertion that proves that for the readiness random forest, stay on this page instead of advancing.

Random forest glossary

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