RRepoGEO

REPOGEO REPORT · LITE

fabsig/GPBoost

Default branch master · commit 1a40406d · scanned 6/10/2026, 12:37:03 PM

GitHub: 686 stars · 56 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
54 /100
Needs work
Category recall
1 / 2
Avg rank #9.0 when recommended
Rule findings
1 pass · 1 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 fabsig/GPBoost, 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 introduction to emphasize Python/R and mixed-effects models

    Why:

    CURRENT
    **GPBoost is a software library for tree-boosting, Gaussian processes, and mixed-effects models.** It allows for combining tree-boosting with Gaussian process and random effects models ( = GPBoost algorithm) as wells as for independently applying Gaussian processes, (generalized) linear mixed effects models (LMMs and GLMMs), and tree-boosting. The GPBoost library is predominantly written in C++, it has a C interface, and there exist both a **Python package** and an **R package**.
    COPY-PASTE FIX
    **GPBoost is a powerful software library for combining tree-boosting with Gaussian processes and mixed-effects models, available as both a Python and R package.** It provides a unified framework for advanced predictive modeling, allowing for the integration of gradient boosting with latent Gaussian variable models (including Gaussian processes and generalized linear mixed-effects models).
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://fabsig.github.io/GPBoost/ (or your project's official documentation/website URL)
  • lowlicense#3
    Clarify the existing license in the README's license section

    Why:

    COPY-PASTE FIX
    Add a sentence to your README's 'License' section, e.g., 'This project is licensed under [Specify License Name(s) and terms, e.g., a custom license combining Apache-2.0 and MIT]. 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
1 / 2
50% of queries surface fabsig/GPBoost
Avg rank
#9.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
XGBoost
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. XGBoost · recommended 2×
  2. scikit-learn · recommended 2×
  3. LightGBM · recommended 2×
  4. CatBoost · recommended 2×
  5. GPyTorch · recommended 1×
  • CATEGORY QUERY
    How to combine tree-boosting algorithms with Gaussian processes for improved predictions?
    you: #9
    AI recommended (in order):
    1. XGBoost
    2. GPyTorch
    3. scikit-learn
    4. GPy
    5. LightGBM
    6. GPflow
    7. mlxtend
    8. CatBoost
    9. GPBoost ← you
    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python library for mixed-effects models and gradient boosting in data science.
    you: not recommended
    AI recommended (in order):
    1. statsmodels
    2. LightGBM
    3. XGBoost
    4. CatBoost
    5. PyMC
    6. scikit-learn

    AI recommended 6 alternatives but never named fabsig/GPBoost. 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 fabsig/GPBoost?
    pass
    AI named fabsig/GPBoost explicitly

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

  • If a team adopts fabsig/GPBoost in production, what risks or prerequisites should they evaluate first?
    pass
    AI named fabsig/GPBoost 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 fabsig/GPBoost solve, and who is the primary audience?
    pass
    AI named fabsig/GPBoost explicitly

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite