RRepoGEO

REPOGEO REPORT · LITE

ModelOriented/DrWhy

Default branch master · commit 2cb4580f · scanned 6/1/2026, 5:57:44 PM

GitHub: 687 stars · 85 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface ModelOriented/DrWhy, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) that aligns with the project's goals.
  • mediumreadme#2
    Reposition the README's opening to clearly state the core purpose

    Why:

    CURRENT
    # Responsible Machine Learning

*With Great Power Comes Great Responsibility*.
Voltaire (well, maybe)

How to develop machine learning models in a responsible manner? There are several topics worth considering:

Effective**. Is the model good enough?...
    COPY-PASTE FIX
    # DrWhy: A Collection of Tools for Explainable AI (XAI)

DrWhy provides a unified framework and simple grammar for the exploration, explanation, and visualization of predictive models, supporting responsible machine learning practices.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface ModelOriented/DrWhy
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SHAP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. SHAP · recommended 1×
  2. LIME · recommended 1×
  3. ELI5 · recommended 1×
  4. InterpretML · recommended 1×
  5. Captum · recommended 1×
  • CATEGORY QUERY
    How to understand why my machine learning model makes specific predictions?
    you: not recommended
    AI recommended (in order):
    1. SHAP
    2. LIME
    3. ELI5
    4. InterpretML
    5. Captum
    6. What-If Tool (WIT)
    7. Skater

    AI recommended 7 alternatives but never named ModelOriented/DrWhy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help ensure fairness and detect bias in AI model outcomes?
    you: not recommended
    AI recommended (in order):
    1. IBM AI Fairness 360 (AIF360) (https://github.com/IBM/AIF360)
    2. Google's What-If Tool (WIT) (https://github.com/PAIR-code/what-if-tool)
    3. Microsoft Fairlearn (https://github.com/fairlearn/fairlearn)
    4. Meta's AI Explainability 360 (AIX360) (https://github.com/IBM/AIX360)
    5. Amazon SageMaker Clarify
    6. Fiddler AI
    7. Dalex (Descriptive mAchine Learning EXplanations) (https://github.com/ModelOriented/DALEX)

    AI recommended 7 alternatives but never named ModelOriented/DrWhy. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of ModelOriented/DrWhy?
    pass
    AI named ModelOriented/DrWhy explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts ModelOriented/DrWhy in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ModelOriented/DrWhy explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo ModelOriented/DrWhy solve, and who is the primary audience?
    pass
    AI named ModelOriented/DrWhy explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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ModelOriented/DrWhy — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite