RRepoGEO

REPOGEO REPORT · LITE

understandable-machine-intelligence-lab/Quantus

Default branch main · commit 2e8d9a31 · scanned 6/16/2026, 11:12:00 AM

GitHub: 666 stars · 91 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 understandable-machine-intelligence-lab/Quantus, 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

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

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's primary heading to clarify evaluation focus

    Why:

    CURRENT
    <h3><b>A toolkit to evaluate neural network explanations</b></h3>
    COPY-PASTE FIX
    <h3><b>Quantus: A comprehensive toolkit for the responsible and quantitative evaluation of neural network explanations</b></h3>
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    deep-learning, explainable-ai, interpretability, machine-learning, pytorch, quantification-evaluation-methods, reproducibility, tensorflow, xai
    COPY-PASTE FIX
    deep-learning, explainable-ai, interpretability, machine-learning, pytorch, quantification-evaluation-methods, reproducibility, tensorflow, xai, responsible-ai, xai-evaluation, explanation-benchmarking
  • lowreadme#3
    Add a section to README clarifying the project's license

    Why:

    COPY-PASTE FIX
    ## License
Quantus is distributed under [describe your specific license terms here, referencing the LICENSE file]. Please refer to the LICENSE file for full details.

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 understandable-machine-intelligence-lab/Quantus
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Qualtrics
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Qualtrics · recommended 1×
  2. SurveyMonkey · recommended 1×
  3. Amazon Mechanical Turk · recommended 1×
  4. Prolific · recommended 1×
  5. pytorch/captum · recommended 1×
  • CATEGORY QUERY
    How can I reliably evaluate the quality of my deep learning model's explanations?
    you: not recommended
    AI recommended (in order):
    1. Qualtrics
    2. SurveyMonkey
    3. Amazon Mechanical Turk
    4. Prolific
    5. Captum (pytorch/captum)
    6. Alibi Explain (SeldonIO/alibi-explain)
    7. SHAP (slundberg/shap)
    8. DoWhy (py-why/dowhy)

    AI recommended 8 alternatives but never named understandable-machine-intelligence-lab/Quantus. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for quantifying explainable AI method performance in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. Captum
    2. XAI (eXplainable AI) by IBM Research
    3. Alibi Explain
    4. SHAP (SHapley Additive exPlanations)
    5. LIME (Local Interpretable Model-agnostic Explanations)
    6. Interpret-Community (Microsoft)

    AI recommended 6 alternatives but never named understandable-machine-intelligence-lab/Quantus. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 understandable-machine-intelligence-lab/Quantus?
    pass
    AI named understandable-machine-intelligence-lab/Quantus explicitly

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

  • If a team adopts understandable-machine-intelligence-lab/Quantus in production, what risks or prerequisites should they evaluate first?
    pass
    AI named understandable-machine-intelligence-lab/Quantus 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 understandable-machine-intelligence-lab/Quantus solve, and who is the primary audience?
    pass
    AI named understandable-machine-intelligence-lab/Quantus explicitly

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

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understandable-machine-intelligence-lab/Quantus — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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