Automatic Piecewise Linear Regression
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.
For further details, see the documentation. You may also read the published article for additional insights: Link 1 and Link 2. Additional functionality has been added since the article was published.
To install APLR, use the following command:
pip install aplr
APLR is available for Windows, most Linux distributions, and macOS.
Example Python scripts are available here.
Consider sponsoring Von Ottenbreit Data Science by clicking the Sponsor button on the repository. Sufficient funding will help maintain and further develop APLR.
For inquiries, please email: ottenbreitdatascience@gmail.com
FAQs
Automatic Piecewise Linear Regression
We found that aplr demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
PyPI confirms no security flaws were exploited in the Ultralytics supply chain attack and highlights improvements for safer package publishing.
Security News
Research
The Socket Research Team breaks down a malicious wrapper package that uses obfuscation to harvest credentials and exfiltrate sensitive data.
Research
Security News
Attackers used a malicious npm package typosquatting a popular ESLint plugin to steal sensitive data, execute commands, and exploit developer systems.