maybe add libfm and libffm to benchmark

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.).

MIT License

1.87k stars 334 forks source link

maybe add libfm and libffm to benchmark #9

Open lihang00 opened 9 years ago

lihang00 commented 9 years ago

http://www.libfm.org/ Factorization machines (FM) are a generic approach that allows to mimic most factorization models by feature engineering.

http://www.csie.ntu.edu.tw/~cjlin/libffm/ LIBFFM is an open source tool for field-aware factorization machines (FFM). For the formulation of FFM, please see these slides. It has been used to win two recent click-through rate prediction competitions (Criteo's and Avazu's).

They are also very interesting tools.

szilard commented 9 years ago

Sure, if someone wants to do this :)

KKulma commented 5 years ago

why remove the labels? Is contribution no longer needed?

szilard commented 5 years ago

Sorry, I don't recall why I removed the labels (it was 2 yrs ago). Btw this repo is more like in legacy mode, see https://github.com/szilard/benchm-ml#summary (in short: on a reduced scope, I maintain https://github.com/szilard/GBM-perf for GBMs)