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.).
RMIT
Stargazers
- alxndrkalininBroad Institute of MIT and Harvard
- AttilaForgacsBudapest
- chaoyue729DX-inc
- chelsyx@Wikimedia
- chlsynerge development services
- darachSquircle Systems Ltd
- daroczig@rxstudioinc
- earinoFoster City, CA
- erfannouryCupertino, CA
- erogolCantina.ai
- eyadsibai
- falconzyxUniversity of Alberta
- goshacmdSeattle, WA
- hankroarkSeattle Area
- hantuzun@neu-fi
- hetong007Amazon Shanghai AI Lab
- hezilaHKBU
- igorbrigadirInsight Centre for Data Analytics
- kudkudakWarsaw
- lihang00
- lmh1020lmh
- mattdowleH2O.ai
- nfultz@njnmco
- npurger
- p-coquelinFrance
- quxiaofengTsinghua University
- RinatMenyashev
- samzhang111Boulder, Colorado
- shicaiHangzhou
- Tafkas@stripe
- whbzjuZhejiang University
- xshhhm
- y-lanTokyo, Japan
- yunchenran
- yutannihilationJapan
- zygmuntz