REPOGEO REPORT · LITE

szilard/benchm-ml

Default branch master · commit 941dfd4e · scanned 6/20/2026, 11:32:45 PM

GitHub: 1,896 stars · 328 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

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

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 szilard/benchm-ml, 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 to clarify historical status and point to successor

Why:

CURRENT

## Simple/limited/incomplete benchmark for scalability, speed and accuracy of machine learning libraries for classification

_**All benchmarks are wrong, but some are useful**_

This project aims at a *minimal* benchmark...

COPY-PASTE FIX

## Simple/limited/incomplete benchmark for scalability, speed and accuracy of machine learning libraries for classification

**IMPORTANT: This repository contains a historical benchmark, largely completed in 2015. For the actively maintained and updated successor project, please refer to [INSERT_LINK_TO_NEW_BENCHMARK_HERE].**

_**All benchmarks are wrong, but some are useful**_

This project aims at a *minimal* benchmark...

mediumhomepage#2
Add homepage URL for the successor project
Why:
COPY-PASTE FIX
```
[INSERT_LINK_TO_NEW_BENCHMARK_HERE]
```

mediumtopics#3

Add 'benchmark' and 'performance-testing' to topics

Why:

CURRENT

data-science, deep-learning, gradient-boosting-machine, h2o, machine-learning, python, r, random-forest, spark, xgboost

COPY-PASTE FIX

benchmark, performance-testing, data-science, deep-learning, gradient-boosting-machine, h2o, machine-learning, python, r, random-forest, spark, 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

0 / 2

0% of queries surface szilard/benchm-ml

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LightGBM

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LightGBM · recommended 2×
XGBoost · recommended 2×
CatBoost · recommended 2×
PyTorch · recommended 2×
scikit-learn · recommended 1×

CATEGORY QUERY
Which machine learning libraries provide the best performance for binary classification on large datasets?
you: not recommended
AI recommended (in order):
1. LightGBM
2. XGBoost
3. CatBoost
4. scikit-learn
5. SGDClassifier
6. LogisticRegression
7. HistGradientBoostingClassifier
8. Apache Spark MLlib
9. TensorFlow
10. PyTorch
AI recommended 10 alternatives but never named szilard/benchm-ml. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How do different open-source ML frameworks scale for binary classification with millions of records?
you: not recommended
AI recommended (in order):
1. XGBoost
2. LightGBM
3. CatBoost
4. Scikit-learn
5. TensorFlow / Keras
6. PyTorch
7. Dask-ML
AI recommended 7 alternatives but never named szilard/benchm-ml. 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 szilard/benchm-ml?
pass
AI named szilard/benchm-ml explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts szilard/benchm-ml in production, what risks or prerequisites should they evaluate first?
pass
AI named szilard/benchm-ml 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 szilard/benchm-ml solve, and who is the primary audience?
pass
AI named szilard/benchm-ml 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 szilard/benchm-ml. 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/szilard/benchm-ml.svg)](https://repogeo.com/en/r/szilard/benchm-ml)

HTML

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

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szilard/benchm-ml — 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