validate-a-machine-learning-feature
Validate a machine-learning feature for meaning, provenance, leakage, timing, training-serving parity, quality, stability, fairness risk, and monitoring. Use when adding or changing an input to a predictive or generative model.
ソース情報
- リポジトリ
- protosphinx/sphinxstack
- ソースの最終更新活動
- 2026年7月29日 13:55
- 検出された SKILL.md の言語
- 英語
- スター
- 3
- フォーク
- 0
インストール方法
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
ソースファイルを確認
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
ファイルエクスプローラー
2 ファイルSKILL.md を表示中
SKILL.md
ソースの指示 · 読み取り専用プレビュー- name
- validate-a-machine-learning-feature
- category
- data
- description
- Validate a machine-learning feature for meaning, provenance, leakage, timing, training-serving parity, quality, stability, fairness risk, and monitoring. Use when adding or changing an input to a predictive or generative model.
# validate-a-machine-learning-feature
Reconstruct what was knowable at prediction time.
## When to use
- Use for batch, streaming, online, derived, embedding, label-adjacent, or third-party features.
- Do not use a feature whose rights, consent, provenance, or prediction-time availability is unresolved.
## Procedure
1. Define feature meaning, entity, source, owner, model use, population, sensitivity, and allowed purpose.
2. Trace provenance, transformations, joins, labels, timestamps, permissions, retention, and versions.
3. Rebuild historical values using only data available before each prediction timestamp.
4. Test target, future, proxy, split, duplicate-entity, and evaluation-set leakage.
5. Compare offline and online definitions, code, defaults, freshness, null handling, types, units, and lookup behavior.
6. Profile coverage, missingness, distribution, outliers, drift, stability, and subgroup differences.
7. Test ablation, sensitivity, robustness, adversarial manipulation, and failure defaults.
8. Define monitoring, alerts, ownership, rollback, and retraining triggers before release.
## Done
- A feature validation report records meaning, provenance, availability, leakage tests, parity, quality, subgroup risk, performance, and ownership
- Historical reconstruction, leakage, offline-online, drift, subgroup, ablation, adversarial, and rollback checks verify fitness
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