APLR

Automatic Piecewise Linear Regression

About

APLR allows you to build predictive and interpretable regression or classification machine learning models in Python, using the Automatic Piecewise Linear Regression (APLR) methodology developed by Mathias von Ottenbreit. APLR often rivals tree-based methods in predictive accuracy, while offering smoother, more interpretable predictions.

Documentation

Resources:

• Presentation and guides
• Examples
• Published Article
• Medium Article

Note: APLR has been updated with additional functionality since the article was published.

API Reference:

• Regression
• Classification
• Tuning

Installation

To install APLR, use the following command:

pip install aplr

To include dependencies for plotting, use this command instead:

pip install aplr[plots]

Availability

APLR is available for Windows, most Linux distributions, and macOS.

Sponsorship

Consider sponsoring Von Ottenbreit Data Science by clicking the Sponsor button on the repository. Sufficient funding will help maintain and further develop APLR.

Contact Information

For inquiries, please email: ottenbreitdatascience@gmail.com

Citation

If you use APLR in your research, please cite the published article:

von Ottenbreit, M., De Bin, R. Automatic piecewise linear regression. Comput Stat 39, 1867–1907 (2024). https://doi.org/10.1007/s00180-024-01475-4