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SeldonIO/alibi
默认分支 master · commit 99c3421d · 扫描时间 2026/7/1 20:31:38
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下方为分数趋势(含全部就绪扫描;左旧右新,可横向滚动)。表格明细默认折叠,展开后每页 10 条,最新在上。
共 2 条就绪扫描。点击下方按钮展开表格(每页 10 条,可翻页)。
行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试:当用户向 AI 提一个不带品牌、本应让 SeldonIO/alibi 浮出水面的问题时,AI 是真的推荐了你,还是推荐了你的竞品?客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。
行动计划 — 可复制粘贴的修复
3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。
- highreadme#1Clarify Alibi's scope in the README to distinguish from alibi-detect
原因:
当前Alibi is a source-available Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. If you're interested in outlier detection, concept drift or adversarial instance detection, check out our sister project alibi-detect.
复制粘贴的修复Alibi is a source-available Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models. **Please note that Alibi focuses exclusively on explanation methods; for outlier detection, concept drift, or adversarial instance detection, refer to our sister project, alibi-detect.**
- mediumreadme#2Add an explicit license statement to the README
原因:
复制粘贴的修复Alibi is released under the terms specified in the [LICENSE file](https://github.com/SeldonIO/alibi/blob/master/LICENSE).
- lowreadme#3Strengthen the README's introductory sentence for better positioning
原因:
当前Alibi is a source-available Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.
复制粘贴的修复Alibi is a comprehensive, source-available Python library providing high-quality implementations of leading machine learning model inspection and interpretation methods. It focuses on robust black-box, white-box, local, and global explanation techniques for classification and regression models.
本次扫描解析到的品类 GEO 通道:google/gemini-2.5-flash, deepseek/deepseek-v4-flash
品类可见性 — 真正的 GEO 测试
向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你,还是推荐了别人?
各模型使用同一组问题 — 切换标签对比回答与排名。
- shap/shap · 被推荐 2 次
- marcotcr/lime · 被推荐 2 次
- microsoft/interpret · 被推荐 2 次
- eli5/eli5 · 被推荐 1 次
- tensorflow/tensorboard · 被推荐 1 次
- 品类问题How can I interpret and inspect the decisions of my black-box machine learning models?你:第 6 位AI 推荐顺序:
- SHAP (shap/shap)
- LIME (marcotcr/lime)
- ELI5 (eli5/eli5)
- InterpretML (microsoft/interpret)
- What-If Tool (tensorflow/tensorboard)
- Alibi Explain (SeldonIO/alibi) ← 你
- Skater (oracle/skater)
查看 AI 完整回答
- 品类问题What are the leading Python frameworks for eXplainable AI (XAI) model interpretability?你:第 6 位AI 推荐顺序:
- SHAP (shap/shap)
- LIME (marcotcr/lime)
- ELI5 (eli5-team/eli5)
- InterpretML (microsoft/interpret)
- Captum (pytorch/captum)
- Alibi Explain (SeldonIO/alibi) ← 你
查看 AI 完整回答
客观检查
针对 AI 引擎最看重的元数据信号的规则审计。
- Metadata completenesspass
- README presencepass
自指检查
当被直接问到你时,AI 是否还知道你的仓库存在?
- Compared to common alternatives in this category, what is the core differentiator of SeldonIO/alibi?passAI 明确点名了 SeldonIO/alibi
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
- If a team adopts SeldonIO/alibi in production, what risks or prerequisites should they evaluate first?passAI 明确点名了 SeldonIO/alibi
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
- In one sentence, what problem does the repo SeldonIO/alibi solve, and who is the primary audience?passAI 明确点名了 SeldonIO/alibi
AI 的回答可能信誓旦旦却是错的。请按事实核对:技术栈、目标人群、差异化点是不是和你实际的对得上?
嵌入你的 GEO 徽章
把这个徽章贴进 SeldonIO/alibi 的 README。每次重新扫描都会自动更新,并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。
[](https://repogeo.com/zh/r/SeldonIO/alibi)<a href="https://repogeo.com/zh/r/SeldonIO/alibi"><img src="https://repogeo.com/badge/SeldonIO/alibi.svg" alt="RepoGEO" /></a>订阅 Pro,解锁深度诊断
SeldonIO/alibi — 轻量扫描仍免费;本卡列出 Pro 相对轻量的深度额度。
- 深度报告每月 10 次
- 无品牌品类查询5,轻量 2
- 优先行动项8,轻量 3