package linwrap

  1. Overview
  2. Docs
Wrapper on top of liblinear-tools

Install

Dune Dependency

Authors

Maintainers

Sources

v9.2.0.tar.gz
sha256=93e4bb71116b5ba3bd0a4baa62ca6521c8b17ade0848299778e0f18ffbd6005a
md5=a61342684e0ba7db2757c7aa60c84744

README.md.html

linwrap

Wrapper on top of liblinear-tools.

Linwrap can be used to train a L2-regularized logistic regression classifier or a linear Support Vector Regressor. You can optimize C (the L2 regularization parameter), w (the class weight) or k (the number of bags, i.e. use bagging). You can also find the optimal classification threshold using MCC maximization, use k-folds cross validation, parallelization, etc. In the regression case, you can only optimize C and epsilon.

Bibliography

[1] Fan, R. E., Chang, K. W., Hsieh, C. J., Wang, X. R., & Lin, C. J. (2008). LIBLINEAR: A library for large linear classification. Journal of machine learning research, 9(Aug), 1871-1874.

[2] Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification.

[3] Hsia, J. Y., & Lin, C. J. (2020). Parameter selection for linear support vector regression. IEEE Transactions on Neural Networks and Learning Systems.

[4] Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.

OCaml

Innovation. Community. Security.