https://github.com/ottenbreit-data-science/aplr
APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.
https://github.com/ottenbreit-data-science/aplr
ai aplr automatic-piecewise-linear-regression classification explainable-ai explainable-boosting generalized-linear-models glm gradient-boosting interpretability interpretable-ai interpretable-machine-learning interpretable-ml linear-regression machine-learning piecewise-regression regression scikit-learn segmented-regression transparency
Last synced: about 2 months ago
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APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.
- Host: GitHub
- URL: https://github.com/ottenbreit-data-science/aplr
- Owner: ottenbreit-data-science
- License: mit
- Created: 2022-05-12T14:12:15.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2026-02-24T17:28:15.000Z (4 months ago)
- Last Synced: 2026-02-24T21:49:30.184Z (4 months ago)
- Topics: ai, aplr, automatic-piecewise-linear-regression, classification, explainable-ai, explainable-boosting, generalized-linear-models, glm, gradient-boosting, interpretability, interpretable-ai, interpretable-machine-learning, interpretable-ml, linear-regression, machine-learning, piecewise-regression, regression, scikit-learn, segmented-regression, transparency
- Language: C++
- Homepage:
- Size: 7.35 MB
- Stars: 23
- Watchers: 0
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.yml
- License: LICENSE
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README
# 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](https://github.com/ottenbreit-data-science/aplr/tree/main/documentation)
- [Examples](https://github.com/ottenbreit-data-science/aplr/tree/main/examples)
- [Published Article](https://rdcu.be/dz7bF)
- [Medium Article](https://medium.com/@ottenbreitdatascience/beyond-black-boxes-high-performance-regression-with-automatic-piecewise-linear-regression-aplr-550fe142160e)
*Note: APLR has been updated with additional functionality since the article was published.*
**API Reference:**
- [Regression](https://github.com/ottenbreit-data-science/aplr/blob/main/API_REFERENCE_FOR_REGRESSION.md)
- [Classification](https://github.com/ottenbreit-data-science/aplr/blob/main/API_REFERENCE_FOR_CLASSIFICATION.md)
- [Tuning](https://github.com/ottenbreit-data-science/aplr/blob/main/API_REFERENCE_FOR_APLR_TUNER.md)
## Installation
To install APLR, use the following command:
```bash
pip install aplr
```
To include dependencies for plotting, use this command instead:
```bash
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](mailto: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