RRepoGEO

REPOGEO REPORT · LITE

Machine-Learning-Tokyo/Interactive_Tools

Default branch master · commit 3ebda94f · scanned 5/25/2026, 11:02:56 AM

GitHub: 2,822 stars · 324 forks

AI VISIBILITY SCORE
22 /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
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 Machine-Learning-Tokyo/Interactive_Tools, 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
  • highlicense#1
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT, Apache-2.0, GPL-3.0) in the repository root to clearly state the terms of use.
  • highreadme#2
    Strengthen README's opening to emphasize the repo as a curated collection

    Why:

    CURRENT
    # Interactive Tools for machine learning, deep learning, and math
    COPY-PASTE FIX
    # Interactive Tools for machine learning, deep learning, and math

A curated collection of interactive web-based tools designed to visually explore and understand complex concepts across machine learning, deep learning, and mathematics.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to a relevant landing page or the GitHub Pages site if one exists for this collection.

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 Machine-Learning-Tokyo/Interactive_Tools
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorBoard
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorBoard · recommended 1×
  2. Netron · recommended 1×
  3. Weights & Biases (W&B) · recommended 1×
  4. DeepView.js · recommended 1×
  5. Lobe · recommended 1×
  • CATEGORY QUERY
    How can I visually explore and understand deep learning model architectures and behaviors?
    you: not recommended
    AI recommended (in order):
    1. TensorBoard
    2. Netron
    3. Weights & Biases (W&B)
    4. DeepView.js
    5. Lobe
    6. Captum
    7. Plotly Dash

    AI recommended 7 alternatives but never named Machine-Learning-Tokyo/Interactive_Tools. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good interactive tools for explaining and interpreting complex machine learning models?
    you: not recommended
    AI recommended (in order):
    1. SHAP (SHapley Additive exPlanations)
    2. LIME (Local Interpretable Model-agnostic Explanations)
    3. What-If Tool (WIT)
    4. InterpretML
    5. ELI5
    6. Yellowbrick
    7. TensorFlow Lite Model Interpretability Library

    AI recommended 7 alternatives but never named Machine-Learning-Tokyo/Interactive_Tools. 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 Machine-Learning-Tokyo/Interactive_Tools?
    pass
    AI did not name Machine-Learning-Tokyo/Interactive_Tools — 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 Machine-Learning-Tokyo/Interactive_Tools in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Machine-Learning-Tokyo/Interactive_Tools 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 Machine-Learning-Tokyo/Interactive_Tools solve, and who is the primary audience?
    pass
    AI did not name Machine-Learning-Tokyo/Interactive_Tools — 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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MARKDOWN (README)
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Machine-Learning-Tokyo/Interactive_Tools — 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