RRepoGEO

REPOGEO REPORT · LITE

parrt/dtreeviz

Default branch master · commit 9680ac83 · scanned 5/28/2026, 10:01:55 PM

GitHub: 3,147 stars · 339 forks

AI VISIBILITY SCORE
56 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
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 parrt/dtreeviz, 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 emphasize specialized tree interpretation

    Why:

    CURRENT
    A python library for decision tree visualization and model interpretation. Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models.
    COPY-PASTE FIX
    dtreeviz is the premier Python library for in-depth, data-centric visualization and interpretation of decision trees and tree-based models (Random Forests, XGBoost, LightGBM, Spark MLlib). Unlike general XAI tools, dtreeviz provides granular, intuitive insights into how *tree models* make predictions, making complex model behavior transparent and understandable for data scientists and ML practitioners.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://dtreeviz.readthedocs.io/
  • mediumtopics#3
    Add more specific topics to clarify its niche in tree-based XAI

    Why:

    CURRENT
    data-science, decision-trees, machine-learning, model-interpretation, python, random-forest, scikit-learn, visualization, xgboost
    COPY-PASTE FIX
    data-science, decision-trees, machine-learning, model-interpretation, python, random-forest, scikit-learn, visualization, xgboost, explainable-ai, xai, tree-visualization, model-explanation

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 parrt/dtreeviz
Avg rank
#3.0
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. LightGBM · recommended 2×
  3. ELI5 · recommended 2×
  4. SHAP · recommended 2×
  5. Graphviz · recommended 1×
  • CATEGORY QUERY
    How can I effectively visualize machine learning decision trees for model interpretation?
    you: #3
    AI recommended (in order):
    1. Graphviz
    2. scikit-learn
    3. DTreeViz ← you
    4. XGBoost
    5. LightGBM
    6. Yellowbrick
    7. ELI5
    8. SHAP
    Show full AI answer
  • CATEGORY QUERY
    Need a Python library to explain and visualize complex tree-based machine learning models.
    you: not recommended
    AI recommended (in order):
    1. SHAP
    2. ELI5
    3. LIME
    4. XGBoost
    5. LightGBM
    6. SkopeRules

    AI recommended 6 alternatives but never named parrt/dtreeviz. 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 parrt/dtreeviz?
    pass
    AI did not name parrt/dtreeviz — 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 parrt/dtreeviz in production, what risks or prerequisites should they evaluate first?
    pass
    AI named parrt/dtreeviz 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 parrt/dtreeviz solve, and who is the primary audience?
    pass
    AI named parrt/dtreeviz explicitly

    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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  • Brand-free category queries5 vs 2 in Lite
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parrt/dtreeviz — RepoGEO report