lr-modeling

lr-modeling is a Model training & evaluation skill for AI agents, published by aliyun in the qwen-dianjin repository on GitHub. Skillspace rates lr-modeling D (65/100) and it passed the safety scan; the rating is based on static analysis only.

【LR评分卡建模】当用户需要"逻辑回归"、"LR建模"、"评分卡"、"scorecard"、"WoE建模"、"跑个LR"时使用。基于 WoE 编码 + Logistic Regression 训练标准评分卡模型，输出系数表、分箱WoE映射、评分卡分数转换。复用平台已有的三段式数据切分、AUC/KS/BCR评估体系、稳定性分析。前提条件：需要已有数据集。**不做超参数调优**；不做特征工程。与 xgb-modeling 的区别：本 Skill 使用逻辑回归+WoE编码，输出可解释的评分卡模型；xgb-modeling 使用 XGBoost 树模型。

Grade
D
Quality
65 / 100
Safety
Passed
GitHub stars
612

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.

View source on GitHubaliyun/qwen-dianjin

Our review

Architecture
94 / 100
Maturity
Basic: a single SKILL.md
Security scan
No issues found
License
Not declared. Check the repository before reusing it.

Not checked yet

  • Category is a classifier prediction; no model has read this skill 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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