REPOGEO REPORT · LITE

jpmml/jpmml-evaluator

Default branch master · commit 23d07613 · scanned 5/29/2026, 10:26:53 PM

GitHub: 903 stars · 255 forks

AI VISIBILITY SCORE

80 /100

Healthy

Category recall

2 / 2

Avg rank #1.0 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 jpmml/jpmml-evaluator, 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

hightopics#1
Add relevant topics to the repository
Why:
COPY-PASTE FIX
```
java, pmml, machine-learning, model-evaluation, predictive-models, jvm, data-science
```

highreadme#2

Strengthen the README's opening statement with core differentiator

Why:

CURRENT

Java Evaluator API for Predictive Model Markup Language (PMML).

COPY-PASTE FIX

JPMML-Evaluator is the de facto reference implementation of the PMML specification for the Java/JVM platform, providing a robust API for evaluating predictive models.

mediumhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
[Insert official project homepage URL here, e.g., https://www.jpmml.org/jpmml-evaluator]
```

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 jpmml/jpmml-evaluator

Avg rank

#1.0

Lower is better. #1 = top recommendation.

Share of voice

13%

Of all named tools, what % are you?

Top rival

Apache Spark MLlib

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Apache Spark MLlib · recommended 1×
ADAPA · recommended 1×
KNIME · recommended 1×
JAXB · recommended 1×
apache/spark · recommended 1×

CATEGORY QUERY
Java library to evaluate predictive models described in Predictive Model Markup Language?
you: #1
AI recommended (in order):
1. JPMML-Evaluator ← you
2. Apache Spark MLlib
3. ADAPA
4. KNIME
5. JAXB
Show full AI answer
CATEGORY QUERY
Need to integrate and run PMML-defined machine learning models within a JVM application.
you: #1
AI recommended (in order):
1. JPMML-Evaluator (jpmml/jpmml-evaluator) ← you
2. Apache Spark MLlib (apache/spark)
3. JPMML-SparkML (jpmml/jpmml-sparkml)
4. H2O.ai (h2oai/h2o-3)
5. KNIME Analytics Platform
6. SAS
7. R
8. pmml package (jpmml/r-pmml)
9. Python
10. sklearn-pmml (jpmml/sklearn-pmml)
11. NYX (nyx-ml/nyx)
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 jpmml/jpmml-evaluator?
pass
AI did not name jpmml/jpmml-evaluator — 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 jpmml/jpmml-evaluator in production, what risks or prerequisites should they evaluate first?
pass
AI named jpmml/jpmml-evaluator 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 jpmml/jpmml-evaluator solve, and who is the primary audience?
pass
AI named jpmml/jpmml-evaluator 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 jpmml/jpmml-evaluator. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/jpmml/jpmml-evaluator.svg)](https://repogeo.com/en/r/jpmml/jpmml-evaluator)

HTML

<a href="https://repogeo.com/en/r/jpmml/jpmml-evaluator"><img src="https://repogeo.com/badge/jpmml/jpmml-evaluator.svg" alt="RepoGEO" /></a>

Pro

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jpmml/jpmml-evaluator — 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