Cyclic Coordinate Descent for Logistic Regression with Lasso regularization

This notebook presents the implementation of Cyclic Coordinate Descent (CCD) algorithm for parameter estimation in regularized logistic regression with l1 (lasso) penalty and compares it with standard logistic regression model without regularization.

In particular it involves the implementation of such algorithm from scratch and comparison to the Logistic Regression available in scikit-learn package.

The details of the data and algorithms used are in the solution.ipynb and in the ./report/report.pdf

Requirements

All the scripts have been executed with Python 3.13.2. Pyenv can be used to easily manage your python installations. It is recommended to create a python environment in the cloned repository:

python -m venv --prompt venv .\venv

Afterwards activate the environment (./venv/Scripts) and install the requirements present in requirements.txt:

pip install -r requirements.txt

All the scripts are formatted with the Black formatter.

Reproducibility

To ensure reproducibility: download the datasets from the provided links, use the same Python version (Requirements) and don't change the seeds. The experiments have been performed on a Windows 11 Intel i5 machine.

Directory structure

.  
├── datasets/  
├── report/  
├── results/  
└── solution.ipynb  

TODO:

1. Place here resulting graphics of comparison and performance.