Xgboost Fastforest
Minimal library code to deploy XGBoost models in C++.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Benchm Ml | 1,839 | 2 years ago | 11 | mit | R | |||||
| 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.). | ||||||||||
| Gbm Perf | 180 | 3 years ago | 30 | mit | HTML | |||||
| Performance of various open source GBM implementations | ||||||||||
| Benchmarks | 157 | 9 months ago | 5 | apache-2.0 | Jupyter Notebook | |||||
| Comparison tools | ||||||||||
| Scikit Learn_bench | 99 | 4 months ago | 18 | apache-2.0 | Python | |||||
| scikit-learn_bench benchmarks various implementations of machine learning algorithms across data analytics frameworks. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms. | ||||||||||
| Autoxgboost | 90 | 4 years ago | 25 | other | R | |||||
| Xgboost Fastforest | 77 | 7 months ago | 2 | mit | C++ | |||||
| Minimal library code to deploy XGBoost models in C++. | ||||||||||
| Fast_retraining | 50 | 5 years ago | mit | Jupyter Notebook | ||||||
| Show how to perform fast retraining with LightGBM in different business cases | ||||||||||
| Xgboost Predictor | 32 | 3 | 3 | 10 months ago | 20 | May 25, 2023 | 6 | apache-2.0 | Java | |
| Go Ml Benchmarks | 24 | 2 years ago | 2 | Go | ||||||
| ⏱ Benchmarks of machine learning inference for Go | ||||||||||
| Gbm Benchmarks | 18 | 3 years ago | 3 | mit | Python | |||||
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