Newton-Raphson Logistic Regression
A simple and fast Python 3+ implementation of logistic regression for association analyses using the Newton-Raphson method.
- Free software: MIT license
- Documentation: https://newton-raphson.readthedocs.io.
pip install git+https://github.com/abrahamnunes/newton-raphson
Basic use case
import numpy as np from sklearn.datasets import make_classification from newton_raphson import logistic_regression # Generate synthetic data X, y = make_classification(n_samples=100, n_features=5) # Perform logistic regression res = logistic_regression(X, y) # Print results res.summary()
- Add unit tests for Hessian scaling factor
- Add unit test for Hessian conditioning
- Add capability to monitor each optimization iteration step
How to Cite
If you use Newton-Raphson in your work, we would very much appreciate the citation, which can be done as follows:
- Abraham Nunes (2018). Newton-Raphson Logistic Regression. Zenodo. http://doi.org/10.5281/zenodo.439989