https://github.com/unixjunkie/linwrap
Wrapper on top of liblinear-tools
https://github.com/unixjunkie/linwrap
classification l2-regularization liblinear linear-svm linear-svr logistic-regression lrl2 machine-learning ocaml-program
Last synced: about 1 month ago
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Wrapper on top of liblinear-tools
- Host: GitHub
- URL: https://github.com/unixjunkie/linwrap
- Owner: UnixJunkie
- Created: 2019-09-09T06:02:00.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-04-22T07:37:18.000Z (about 1 year ago)
- Last Synced: 2025-03-27T19:23:07.078Z (2 months ago)
- Topics: classification, l2-regularization, liblinear, linear-svm, linear-svr, logistic-regression, lrl2, machine-learning, ocaml-program
- Language: OCaml
- Homepage:
- Size: 2.6 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 10
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Metadata Files:
- Readme: README.md
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README
# 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.