RRepoGEO

REPOGEO REPORT · LITE

lopusz/awesome-interpretable-machine-learning

Default branch master · commit 3c629a28 · scanned 6/2/2026, 10:22:38 AM

GitHub: 916 stars · 141 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 lopusz/awesome-interpretable-machine-learning, 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
  • highabout#1
    Add a concise "About" description

    Why:

    COPY-PASTE FIX
    An opinionated list of curated resources, papers, and tools for interpretable machine learning (introspection, simplification, visualization, explanation).
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License) in the repository root.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://awesome.re/

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 lopusz/awesome-interpretable-machine-learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SHAP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SHAP · recommended 2×
  2. LIME · recommended 2×
  3. ELI5 · recommended 2×
  4. InterpretML · recommended 2×
  5. What-If Tool · recommended 1×
  • CATEGORY QUERY
    How can I make my complex machine learning models more understandable and transparent for stakeholders?
    you: not recommended
    AI recommended (in order):
    1. SHAP
    2. LIME
    3. ELI5
    4. InterpretML
    5. What-If Tool
    6. Dalex
    7. Skater

    AI recommended 7 alternatives but never named lopusz/awesome-interpretable-machine-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best resources for implementing explainable AI techniques in my data science workflows?
    you: not recommended
    AI recommended (in order):
    1. LIME
    2. SHAP
    3. InterpretML
    4. ELI5
    5. Captum
    6. Alibi Explain

    AI recommended 6 alternatives but never named lopusz/awesome-interpretable-machine-learning. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 lopusz/awesome-interpretable-machine-learning?
    pass
    AI did not name lopusz/awesome-interpretable-machine-learning — likely talking about a different project

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

  • If a team adopts lopusz/awesome-interpretable-machine-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lopusz/awesome-interpretable-machine-learning 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 lopusz/awesome-interpretable-machine-learning solve, and who is the primary audience?
    pass
    AI did not name lopusz/awesome-interpretable-machine-learning — likely talking about a different project

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

Embed your GEO score

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lopusz/awesome-interpretable-machine-learning — 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