- OCaml 91.9%
- Python 5.7%
- Shell 2%
- Makefile 0.4%
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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.