close-the-review-item-on-its-object-not-its-verb

Solid

Use whenever a reviewer, critic or supervisor returns numbered suggestions and you are about to record which were executed, deferred, declined or replaced, and whenever you mark one of your own registered hypotheses or planned experiments as done. Covers the substitution that makes a false close-out read as true - same verb, cheaper object, a tenth of the scale - the four close-out fields that make it impossible to perform by accident, and the two searches that show whether the mechanism you claimed is in code and in your runs' recorded configuration, or only in prose.

AI & Automation 805 stars 25 forks Updated 2 weeks ago NOASSERTION

Install

View on GitHub

Quality Score: 82/100

Stars 20%
97
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Close a review item on its object, not on its verb An actionable suggestion has four parts: > **verb** — cross-validate / ablate / refit / benchmark against > **object** — the model training / the combining rule / the preprocessing > **scale** — over all N labelled rows / on the small split / for three sizes > **price** — about five extra fits / one hour / one more backbone The failure this skill exists for keeps the verb and quietly swaps the object for something you already have, or the scale for one you can afford this minute. You then do real work, it is real work of the named kind, and you record the item as executed. No sentence in that record is false. The item is nevertheless gone, and items are the one class of note nobody re-reads once they are closed. ## What it looks like from inside Measured on one run, verbatim. One hour and thirty-nine minutes into a ten-hour budget, at the close of its first stage, the reviewer wrote: > *"Replace validation-argmax selection with repeated k-fold cross-validation over > train+validation. At 495 rows the current split cannot separate a 1-point gap, > which is wider than the entire spread between this run's five arms; 5-fold CV > over 4934 labelled pairs would cost about five fine-tunes and would make every > subsequent comparison decidable rather than noise-limited."* Verb: cross-validate. Object: the model training. Scale: 4,934 rows. Price: five fits. What got built, fifty minutes later, was a bootstrap comparison of ...

Details

Author
tangxiangru
Repository
tangxiangru/AutoR
Created
6 months ago
Last Updated
2 weeks ago
Language
Python
License
NOASSERTION

Integrates with

Similar Skills

Semantically similar based on skill content — not just same category