RRepoGEO

REPOGEO REPORT · LITE

scikit-learn-contrib/MAPIE

Default branch master · commit 0e39e927 · scanned 5/22/2026, 5:22:45 PM

GitHub: 1,547 stars · 140 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
92 /100
Healthy
Category recall
2 / 2
Avg rank #1.0 when recommended
Rule findings
2 pass · 0 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 scikit-learn-contrib/MAPIE, 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 "statistically guaranteed" to the repository description

    Why:

    CURRENT
    A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.
    COPY-PASTE FIX
    A scikit-learn-compatible library for estimating *statistically guaranteed* prediction intervals and controlling risks, based on conformal predictions.
  • mediumtopics#2
    Add more specific topics for uncertainty quantification and prediction intervals

    Why:

    CURRENT
    classification, confidence-intervals, conformal-prediction, data-science, python, regression, risk-control, sklearn
    COPY-PASTE FIX
    classification, confidence-intervals, conformal-prediction, data-science, python, regression, risk-control, sklearn, uncertainty-quantification, prediction-intervals
  • lowreadme#3
    Add a "Why MAPIE?" section to explicitly compare against common ML models

    Why:

    COPY-PASTE FIX
    ## Why MAPIE?

While many machine learning models can provide basic prediction intervals, MAPIE stands out by offering *statistically guaranteed* prediction intervals and regions. It achieves this through model-agnostic conformal prediction, ensuring robust and reliable uncertainty quantification that goes beyond heuristic approaches found in standard estimators like Gradient Boosting or XGBoost.

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
2 / 2
100% of queries surface scikit-learn-contrib/MAPIE
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
14%
Of all named tools, what % are you?
Top rival
nonconformist
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. nonconformist · recommended 2×
  2. sklearn.ensemble.GradientBoostingRegressor · recommended 1×
  3. LightGBM · recommended 1×
  4. XGBoost · recommended 1×
  5. NGBoost · recommended 1×
  • CATEGORY QUERY
    How to generate robust prediction intervals for scikit-learn models in Python?
    you: #1
    AI recommended (in order):
    1. Mapie ← you
    2. sklearn.ensemble.GradientBoostingRegressor
    3. LightGBM
    4. XGBoost
    5. NGBoost
    6. PyTorch Forecasting
    7. statsmodels
    8. scikit-learn.utils.resample
    9. nonconformist
    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python library for conformal prediction to control risks and quantify uncertainty.
    you: #1
    AI recommended (in order):
    1. Mapie ← you
    2. nonconformist
    3. crepes
    4. Conformal-P-Values
    5. scikit-learn-extra
    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 scikit-learn-contrib/MAPIE?
    pass
    AI named scikit-learn-contrib/MAPIE explicitly

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

  • If a team adopts scikit-learn-contrib/MAPIE in production, what risks or prerequisites should they evaluate first?
    pass
    AI named scikit-learn-contrib/MAPIE 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 scikit-learn-contrib/MAPIE solve, and who is the primary audience?
    pass
    AI named scikit-learn-contrib/MAPIE 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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