Optimization-L1-regularization

Author: Jay Liao (re6094028@gs.ncku.edu.tw)

This is a mid-term project of Optimization Methods, a course of Institute of Data Science, National Cheng Kung University, Taiwan. This project aimed to construct the linear regression with L1 regularization and the logistic regression with L1 regularization.

Data

• ./data/wave_2_classes_with_irrelevant_attributes.arff

Code

• args.py: define the arguments parser
• utils.py: little tools
• main.py: the main program

Usage

1. Clone this repo.

git clone https://github.com/jayenliao/Optimization-L1-regularization.git

2. Run the experiments.

cd Optimization-L1-regularization
python3 main.py

Reference

1. Hutcheson, G. D. (2011). Ordinary least-squares regression. L. Moutinho and GD Hutcheson, The SAGE dictionary of quantitative management research, 224-228.

2. Endelman, J. B., & Jannink, J. L. (2012). Shrinkage estimation of the realized relationship matrix. G3: Genes| genomes| genetics, 2(11), 1405-1413.

3. Helland, I. S. (1987). On the interpretation and use of R2 in regression analysis. Biometrics, 61-69.

4. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288.

5. Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1.

6. Breiman, L., & Ihaka, R. (1984). Nonlinear discriminant analysis via scaling and ACE. Department of Statistics, University of California.

7. Rakotomalala, R. (2005). TANAGRA: une plate-forme d’expérimentation pour la fouille de données. Revue MODULAD, 32, 70-85.