RRepoGEO

REPOGEO REPORT · LITE

oegedijk/explainerdashboard

Default branch master · commit 678e695d · scanned 6/19/2026, 10:21:43 PM

GitHub: 2,486 stars · 345 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
33 /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
2 / 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 oegedijk/explainerdashboard, 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
    Reposition README opening to clearly state XAI dashboard specialization

    Why:

    CURRENT
    This package makes it convenient to quickly deploy a dashboard web app that explains the workings of a (scikit-learn compatible) machine learning model.
    COPY-PASTE FIX
    ExplainerDashboard is a specialized Python library for rapidly building interactive web dashboards dedicated to explaining the inner workings and predictions of 'blackbox' machine learning models. It integrates various XAI methods (like SHAP, LIME, permutation importance) into a single, user-friendly interface with minimal setup, making it ideal for data scientists.
  • mediumtopics#2
    Add more specific XAI dashboard topics

    Why:

    CURRENT
    dash, dashboard, data-scientists, explainer, inner-workings, interactive-dashboards, interactive-plots, model-predictions, permutation-importances, plotly, shap, shap-values, xai, xai-library
    COPY-PASTE FIX
    dash, dashboard, data-scientists, explainer, inner-workings, interactive-dashboards, interactive-plots, model-predictions, permutation-importances, plotly, shap, shap-values, xai, xai-library, xai-dashboard-builder, machine-learning-explanation-dashboard, model-interpretability-tool
  • lowcomparison#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., "## ExplainerDashboard vs. General Dashboarding Tools" or "## Why ExplainerDashboard?", explicitly outlining how it differs from tools like Streamlit, Dash, and Gradio by focusing solely on comprehensive XAI features and rapid deployment for model explanations.

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 oegedijk/explainerdashboard
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Streamlit
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Streamlit · recommended 2×
  2. Dash · recommended 2×
  3. Gradio · recommended 2×
  4. Tableau · recommended 2×
  5. Power BI · recommended 2×
  • CATEGORY QUERY
    How to visualize and explain machine learning model predictions with an interactive dashboard?
    you: not recommended
    AI recommended (in order):
    1. Streamlit
    2. SHAP
    3. LIME
    4. Dash
    5. ELI5
    6. Skater
    7. Gradio
    8. InterpretML
    9. Tableau
    10. TabPy
    11. Power BI
    12. H2O.ai Wave
    13. H2O.ai Explainable AI (XAI)

    AI recommended 13 alternatives but never named oegedijk/explainerdashboard. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good tools for building interactive dashboards to interpret black-box machine learning models?
    you: not recommended
    AI recommended (in order):
    1. Streamlit
    2. Gradio
    3. Dash
    4. Panel
    5. Looker Studio
    6. Tableau
    7. Power BI

    AI recommended 7 alternatives but never named oegedijk/explainerdashboard. 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 oegedijk/explainerdashboard?
    pass
    AI did not name oegedijk/explainerdashboard — 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 oegedijk/explainerdashboard in production, what risks or prerequisites should they evaluate first?
    pass
    AI named oegedijk/explainerdashboard 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 oegedijk/explainerdashboard solve, and who is the primary audience?
    pass
    AI named oegedijk/explainerdashboard explicitly

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

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oegedijk/explainerdashboard — 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