szilard/benchm-ml
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Star & Fork Trend (19 data points)
Multi-Source Signals
Growth Velocity
szilard/benchm-ml has +0 stars this period . Velocity data will be available after more historical data is collected.
Deep analysis is being generated for this repository.
Signal-backed technical analysis will be available soon.
| Metric | benchm-ml | machine-learning-refined | OpenAI-API-dotnet | NCRFpp |
|---|---|---|---|---|
| Stars | 1.9k | 1.9k | 1.9k | 1.9k |
| Forks | 330 | 636 | 422 | 442 |
| Weekly Growth | +0 | +0 | -1 | +0 |
| Language | R | Python | C# | Python |
| Sources | 1 | 1 | 1 | 1 |
| License | MIT | NOASSERTION | NOASSERTION | Apache-2.0 |
Capability Radar vs machine-learning-refined
Last code push 1300 days ago.
Fork-to-star ratio: 17.4%. Active community forking and contributing.
Issue data not yet available.
No measurable growth in the current period (first-day cold start expected).
Licensed under MIT. Permissive — safe for commercial use.
Risk scores are computed from real-time repository data. Higher scores indicate healthier metrics.