szilard/benchm-ml

• highreadme#1

  Reposition README H1 and opening to clarify historical benchmark status

  原因：

  当前

  ## 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 for scalability, speed and accuracy of commonly used implementations...

  复制粘贴的修复

  复制

  ## Historical Benchmark: Scalability, Speed, and Accuracy of Machine Learning Libraries (2015-2017)
  
  **This project is a historical benchmark, largely completed in 2015 with updates until 2017. For a more current benchmark, please refer to the link provided at the end of this README.**
  
  _**All benchmarks are wrong, but some are useful**_
  
  This project aimed at a *minimal* benchmark for scalability, speed and accuracy of commonly used implementations...

  待办完成忽略
• mediumtopics#2

  Add specific topics to emphasize 'benchmark' and 'historical'

  原因：

  当前

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

  复制粘贴的修复

  复制

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

  待办完成忽略
• mediumhomepage#3

  Add a homepage URL to the repository metadata

  原因：

  复制粘贴的修复

  复制

  Add the URL of the newer benchmark project (as referenced in the README) to the repository's homepage field in GitHub settings. Example: `https://github.com/your-org/your-new-benchmark`

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  Which machine learning libraries offer the best performance for binary classification tasks?

  你：未被推荐
  AI 推荐顺序：

  1. XGBoost
  2. LightGBM
  3. CatBoost
  4. scikit-learn
  5. TensorFlow
  6. Keras
  7. PyTorch

  AI 推荐了 7 个替代方案，却始终没点名 szilard/benchm-ml。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  How do different Python and R machine learning libraries scale for large datasets?

  你：未被推荐
  AI 推荐顺序：

  1. Dask
  2. PySpark
  3. XGBoost
  4. LightGBM
  5. CatBoost
  6. Vaex
  7. data.table
  8. SparkR
  9. sparklyr
  10. xgboost
  11. lightgbm
  12. bigmemory
  13. ff
  14. H2O

  AI 推荐了 14 个替代方案，却始终没点名 szilard/benchm-ml。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of szilard/benchm-ml?

  pass
  ⚠AI 未点名 szilard/benchm-ml —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts szilard/benchm-ml in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 szilard/benchm-ml

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo szilard/benchm-ml solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 szilard/benchm-ml

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？