Ship and explain the ThresholdModel class

A shippable lab artifact includes: how to run it, the metric result (assert Strict(0.8) and Loose(0.4) disagree on score 0.5), the failure you can still reproduce (shared mutable class attribute for threshold so instances overwrite each other), the security gate for storing PII inside repr or dict dumps in logs, and a rollback note. The user decision it supports remains: reuse one predict method across two named models with different thresholds.

print({'artifact':'ThresholdModel','proved':['per-instance thresholds','predict reuse'],'unproved':['persistence layer'],'owner':'ml-lab'})

Expected evidence: class ship checklist. Store this beside the fixture version so scores remain meaningful after content changes in python-classes.

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

Fill this table in your notes for the ThresholdModel class:

• Proved on Strict(threshold=0.8), Loose(threshold=0.4), scores=[0.5,0.9]: …
• Unproved beyond the fixture: …
• Metric that blocks release: assert Strict(0.8) and Loose(0.4) disagree on score 0.5
• Failure still reproducible: shared mutable class attribute for threshold so instances overwrite each other
• Security gate: storing PII inside repr or dict dumps in logs
• Rollback: …

Ship the narrative only when the unproved list is honest. Reviewers trust narrow claims that support reuse one predict method across two named models with different thresholds 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 ThresholdModel class against Strict(threshold=0.8), Loose(threshold=0.4), scores=[0.5,0.9].

At the mastery and shipping stage for python-classes, the job is narrower than finishing a product demo. You are creating one progressive evidence piece about Strict(threshold=0.8), Loose(threshold=0.4), scores=(0.5,0.9) 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: two handwritten prediction tables before coding the class.

For this page specifically, success looks like a proved/unproved ship note with rollback while still centering the user decision to reuse one predict method across two named models with different thresholds. If you cannot point to a file, command, or assertion that proves that for the ThresholdModel class, stay on this page instead of advancing.

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