cv-per-label-platt-isotonic-calibration
cv-per-label-platt-isotonic-calibration is a Model training & evaluation skill for AI agents, published by wenmin-wu in the ds-skills repository on GitHub. Skillspace rates cv-per-label-platt-isotonic-calibration D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Fit a per-label probability calibrator on out-of-fold scores using Platt scaling (logistic regression on raw scores) and fall back to isotonic regression for labels where the logistic doesn't converge — pickle the dict of fitted calibrators and apply at inference for a small but free leaderboard lift on multi-label classification
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 52
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 1 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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