RRepoGEO

REPOGEO REPORT · LITE

szilard/benchm-ml

Default branch master · commit 941dfd4e · scanned 5/10/2026, 10:08:04 PM

GitHub: 1,895 stars · 328 forks

AI VISIBILITY SCORE
28 /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
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 szilard/benchm-ml, 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 H1 and opening to clarify historical benchmark status

    Why:

    CURRENT
    ## Simple/limited/incomplete benchmark for scalability, speed and accuracy of machine learning libraries for classification

_**All benchmarks are wrong, but some are useful**_

This project aims at a *minimal* benchmark for scalability, speed and accuracy of commonly used implementations...
    COPY-PASTE FIX
    ## Historical Benchmark: Scalability, Speed, and Accuracy of Machine Learning Libraries (2015-2017)

**This project is a historical benchmark, largely completed in 2015 with updates until 2017. For a more current benchmark, please refer to the link provided at the end of this README.**

_**All benchmarks are wrong, but some are useful**_

This project aimed at a *minimal* benchmark for scalability, speed and accuracy of commonly used implementations...
  • mediumtopics#2
    Add specific topics to emphasize 'benchmark' and 'historical'

    Why:

    CURRENT
    data-science, deep-learning, gradient-boosting-machine, h2o, machine-learning, python, r, random-forest, spark, xgboost
    COPY-PASTE FIX
    data-science, deep-learning, gradient-boosting-machine, h2o, machine-learning, python, r, random-forest, spark, xgboost, benchmark, performance-comparison, historical-benchmark
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the URL of the newer benchmark project (as referenced in the README) to the repository's homepage field in GitHub settings. Example: `https://github.com/your-org/your-new-benchmark`

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 szilard/benchm-ml
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
XGBoost
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. XGBoost · recommended 2×
  2. LightGBM · recommended 2×
  3. CatBoost · recommended 2×
  4. scikit-learn · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    Which machine learning libraries offer the best performance for binary classification tasks?
    you: not recommended
    AI recommended (in order):
    1. XGBoost
    2. LightGBM
    3. CatBoost
    4. scikit-learn
    5. TensorFlow
    6. Keras
    7. PyTorch

    AI recommended 7 alternatives but never named szilard/benchm-ml. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How do different Python and R machine learning libraries scale for large datasets?
    you: not recommended
    AI recommended (in order):
    1. Dask
    2. PySpark
    3. XGBoost
    4. LightGBM
    5. CatBoost
    6. Vaex
    7. data.table
    8. SparkR
    9. sparklyr
    10. xgboost
    11. lightgbm
    12. bigmemory
    13. ff
    14. H2O

    AI recommended 14 alternatives but never named szilard/benchm-ml. 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 szilard/benchm-ml?
    pass
    AI did not name szilard/benchm-ml — 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 szilard/benchm-ml in production, what risks or prerequisites should they evaluate first?
    pass
    AI named szilard/benchm-ml 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 szilard/benchm-ml solve, and who is the primary audience?
    pass
    AI named szilard/benchm-ml explicitly

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

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  • Brand-free category queries5 vs 2 in Lite
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