RRepoGEO

REPOGEO REPORT · LITE

haifengl/smile

Default branch master · commit 3ccac2f8 · scanned 5/26/2026, 7:16:28 PM

GitHub: 6,378 stars · 1,148 forks

AI VISIBILITY SCORE
59 /100
Needs work
Category recall
1 / 2
Avg rank #6.0 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 haifengl/smile, 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
    Strengthen README's opening to clarify repo identity and core differentiator

    Why:

    CURRENT
    SMILE (Statistical Machine Intelligence & Learning Engine) is a comprehensive, high-performance machine learning framework for the JVM.
    COPY-PASTE FIX
    This is the official GitHub repository for SMILE (Statistical Machine Intelligence & Learning Engine), a comprehensive, high-performance, and production-ready machine learning framework written entirely in Java for the JVM.
  • mediumreadme#2
    Add explicit license clarification to README

    Why:

    COPY-PASTE FIX
    SMILE is distributed under [insert specific license name(s) here, e.g., Apache 2.0 and GPLv3]. Please see the LICENSE file for full details.
  • lowtopics#3
    Add specific keywords to repository topics

    Why:

    CURRENT
    classification, clustering, computer-algebra-system, computer-vision, data-science, dataframe, deep-learning, genetic-algorithm, interpolation, linear-algebra, llm, machine-learning, manifold-learning, multidimensional-scaling, nearest-neighbor-search, nlp, regression, statistics, visualization, wavelet
    COPY-PASTE FIX
    classification, clustering, computer-algebra-system, computer-vision, data-science, dataframe, deep-learning, genetic-algorithm, interpolation, linear-algebra, llm, machine-learning, manifold-learning, multidimensional-scaling, nearest-neighbor-search, nlp, regression, statistics, visualization, wavelet, jvm, statistical-modeling, data-analysis

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 haifengl/smile
Avg rank
#6.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
Apache Spark MLlib
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Apache Spark MLlib · recommended 2×
  2. Deeplearning4j · recommended 1×
  3. H2O.ai · recommended 1×
  4. Weka · recommended 1×
  5. Tribuo · recommended 1×
  • CATEGORY QUERY
    What are the best machine learning libraries for JVM-based applications with comprehensive features?
    you: #6
    AI recommended (in order):
    1. Deeplearning4j
    2. Apache Spark MLlib
    3. H2O.ai
    4. Weka
    5. Tribuo
    6. Smile ← you
    Show full AI answer
  • CATEGORY QUERY
    Seeking a robust framework for statistical modeling, data analysis, and deep learning on the JVM.
    you: not recommended
    AI recommended (in order):
    1. Deeplearning4j (DL4J)
    2. Apache Spark MLlib
    3. KotlinDL
    4. Smile (Statistical Machine Intelligence and Learning Engine)
    5. Weka (Waikato Environment for Knowledge Analysis)

    AI recommended 5 alternatives but never named haifengl/smile. This is the gap to close.

    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 haifengl/smile?
    pass
    AI named haifengl/smile explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of haifengl/smile. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/haifengl/smile.svg)](https://repogeo.com/en/r/haifengl/smile)
HTML
<a href="https://repogeo.com/en/r/haifengl/smile"><img src="https://repogeo.com/badge/haifengl/smile.svg" alt="RepoGEO" /></a>
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haifengl/smile — 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