Ship and explain the readiness decision tree

A shippable lab artifact includes: how to run it, the metric result (held-out accuracy plus a tree text dump that mentions both features), the failure you can still reproduce (overfit tree with max_depth unrestricted on tiny data, or missing feature names in export), the security gate for training on labels that embed sensitive attributes without documenting them, and a rollback note. The user decision it supports remains: predict readiness with a path a human can read aloud.

print({'artifact':'readiness tree','proved':['export_text path','holdout acc'],'unproved':['new cohorts'],'owner':'ml-lab'})

Expected evidence: tree ship note. Store this beside the fixture version so scores remain meaningful after content changes in decision-trees.

State operating limits for the readiness decision tree in plain language: fixture size, offline vs live dependencies, and what would require a new eval set. Shipping decision-trees is honest scoping, not maximal confidence language.

Fill this table in your notes for the readiness decision tree:

• Proved on small hours/practice_tests readiness table with one holdout row: …
• Unproved beyond the fixture: …
• Metric that blocks release: held-out accuracy plus a tree text dump that mentions both features
• Failure still reproducible: overfit tree with max_depth unrestricted on tiny data, or missing feature names in export
• Security gate: training on labels that embed sensitive attributes without documenting them
• Rollback: …

Ship the narrative only when the unproved list is honest. Reviewers trust narrow claims that support predict readiness with a path a human can read aloud more than maximal language that collapses under the first production oddity.

Hand your ship note to a peer and ask them to recreate a proved/unproved ship note with rollback without watching you type. If they cannot, your evidence is still tribal knowledge. Tighten the run command and the metric line until a stranger can validate the readiness decision tree against small hours/practice_tests readiness table with one holdout row.

At the mastery and shipping stage for decision-trees, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about small hours/practice_tests readiness table with one holdout 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: majority-class accuracy on the holdout before fitting the tree.

For this page specifically, success looks like a proved/unproved ship note with rollback while still centering the user decision to predict readiness with a path a human can read aloud. If you cannot point to a file, command, or assertion that proves that for the readiness decision tree, stay on this page instead of advancing.

Decision tree glossary

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