Date of creation: 23. 5. 2017
It is possible to download software here.
Regularized linear regression tool (regGLM) provides a fast and easy way of performing the regression analysis using modern algorithms: linear regression, L1-regularized linear regression (LASSO regression) and L2-regularized linear regression (Ridge regression), and optimization techniques: gradient/coordinate descent. Algorithms LASSO and Ridge regression uses L1/L2 vector norms to optimize the regression coefficients in order to balance between bias and variance of the model.
The regGLM tool is programmed in the Python programming language (ver. 3.5.2). It provides a wide spectrum of outputs that can be used to assess the validity of the regression model: cost function evolution graph (functioning assessment), residual graph (validity assessment), coefficients graph (model visualization), etc. The tool also provides the data that can be used to test its functionalities. The data are based on the real-world example of retail-sales price estimation.
This work was supported by projects AZV 16-30805A and LO1401. The described research was performed in laboratories supported by the SIX project; the registration number CZ.1.05/2.1.00/03.0072, the operational program Research and Development for Innovation.