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ModelOriented/DALEX

默认分支 master · commit c4791abc · 扫描时间 2026/5/9 23:51:47

星标 1,467 · Fork 170

AI 可见性总分
40 /100
亟需修复
品类召回
0 / 2
在所有问题中均未被推荐
规则结果
通过 2 · 警告 0 · 失败 0
客观元数据检查
AI 认识你的名字
3 / 3
直接询问时,AI 是否点名你的仓库
如何阅读这份报告

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 ModelOriented/DALEX 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。

整体方向
  • highreadme#1
    Strengthen README's opening statement for immediate value proposition

    原因:

    当前
    # moDel Agnostic Language for Exploration and eXplanation 
    
    ... 
    
    ## Overview
    
    Unverified black box model is the path to the failure. Opaqueness leads to distrust. Distrust leads to ignoration. Ignoration leads to rejection.
    
    The `DALEX` package xrays any model and helps to explore and explain its behaviour, helps to understand how complex models are working.
    复制粘贴的修复
    # DALEX: Model-Agnostic Language for Exploration and eXplanation
    
    **DALEX is a powerful R and Python package for Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI). It helps data scientists and machine learning engineers understand, explain, and diagnose complex black-box models, providing a unified framework for model-agnostic interpretability.**
  • mediumreadme#2
    Explicitly mention fairness and visualization capabilities in README

    原因:

    当前
    The `DALEX` package xrays any model and helps to explore and explain its behaviour, helps to understand how complex models are working. The main function `explain()` creates a wrapper around a predictive model. Wrapped models may then be explored and compared with a collection of local and global explainers.
    复制粘贴的修复
    The `DALEX` package xrays any model and helps to explore and explain its behaviour, helps to understand how complex models are working. The main function `explain()` creates a wrapper around a predictive model. Wrapped models may then be explored and compared with a collection of local and global explainers, **offering powerful visualization tools and methods to assess model fairness and identify potential biases.**
  • lowreadme#3
    Add a 'Resources' section to the README

    原因:

    复制粘贴的修复
    ## Resources
    
    *   **Explanatory Model Analysis e-book:** The philosophy behind DALEX explanations is described in this e-book. Find it at [https://dalex.drwhy.ai/](https://dalex.drwhy.ai/)
    *   **DrWhy.AI Universe:** DALEX is a part of the broader DrWhy.AI ecosystem. Explore more at [http://drwhy.ai/#BackBone](http://drwhy.ai/#BackBone)

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。

召回
0 / 2
0% 的问题里出现了 ModelOriented/DALEX
平均排名
越小越好。#1 表示首位推荐。
声量占比
0%
在所有被点名的工具中,你占了多少?
头号对手
SHAP
在 2 个问题中被推荐 1 次
竞品排行
  1. SHAP · 被推荐 1 次
  2. LIME · 被推荐 1 次
  3. ELI5 · 被推荐 1 次
  4. InterpretML · 被推荐 1 次
  5. What-If Tool · 被推荐 1 次
  • 品类问题
    How to interpret complex black-box machine learning model predictions for better understanding?
    你:未被推荐
    AI 推荐顺序:
    1. SHAP
    2. LIME
    3. ELI5
    4. InterpretML
    5. What-If Tool
    6. Alibi Explain
    7. Captum

    AI 推荐了 7 个替代方案,却始终没点名 ModelOriented/DALEX。这就是要补上的差距。

    查看 AI 完整回答
  • 品类问题
    What tools help visualize and compare different machine learning model explanations for fairness?
    你:未被推荐
    AI 推荐顺序:
    1. IBM AI Fairness 360 (AIF360) (IBM/AIF360)
    2. Microsoft Fairlearn (fairlearn/fairlearn)
    3. Google What-If Tool (WIT) (PAIR-code/what-if-tool)
    4. SHAP (SHapley Additive exPlanations) (shap/shap)
    5. LIME (Local Interpretable Model-agnostic Explanations) (marcotcr/lime)
    6. InterpretML (interpretml/interpret)

    AI 推荐了 6 个替代方案,却始终没点名 ModelOriented/DALEX。这就是要补上的差距。

    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 completeness
    pass

  • README presence
    pass

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 ModelOriented/DALEX?
    pass
    AI 明确点名了 ModelOriented/DALEX

    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 ModelOriented/DALEX in production, what risks or prerequisites should they evaluate first?
    pass
    AI 明确点名了 ModelOriented/DALEX

    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 ModelOriented/DALEX solve, and who is the primary audience?
    pass
    AI 明确点名了 ModelOriented/DALEX

    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 ModelOriented/DALEX 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。

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Pro

订阅 Pro,解锁深度诊断

ModelOriented/DALEX — 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