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 artificial-intelligence classification explainability explainable-ai explainable-ml general-additive-model generalized-linear-models glm gradient-boosting interpretability interpretable-ai interpretable-machine-learning interpretable-ml linear-regression machine-learning piecewise-linear-regression regression scikit-learn 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 (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-04-17T08:23:19.000Z (6 months ago)
- Last Synced: 2025-06-28T06:20:48.030Z (3 months ago)
- Topics: ai, artificial-intelligence, classification, explainability, explainable-ai, explainable-ml, general-additive-model, generalized-linear-models, glm, gradient-boosting, interpretability, interpretable-ai, interpretable-machine-learning, interpretable-ml, linear-regression, machine-learning, piecewise-linear-regression, regression, scikit-learn, transparency
- Language: C++
- Homepage:
- Size: 6.13 MB
- Stars: 20
- Watchers: 0
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.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.
For further details, see the [documentation](https://github.com/ottenbreit-data-science/aplr/tree/main/documentation). You may also read the published article for additional insights: [Link 1](https://link.springer.com/article/10.1007/s00180-024-01475-4) and [Link 2](https://rdcu.be/dz7bF). Additional functionality has been added since the article was published.
## Installation
To install APLR, use the following command:
```bash
pip install aplr
```
## Availability
APLR is available for Windows, most Linux distributions, and macOS.
## Usage
Example Python scripts are available [here](https://github.com/ottenbreit-data-science/aplr/tree/main/examples).
## Sponsorship
Consider sponsoring Von Ottenbreit Data Science by clicking the **Sponsor** button on the repository. Sufficient funding will help maintain and further develop APLR.
## API Reference
- [API reference for regression](https://github.com/ottenbreit-data-science/aplr/blob/main/API_REFERENCE_FOR_REGRESSION.md)
- [API reference for classification](https://github.com/ottenbreit-data-science/aplr/blob/main/API_REFERENCE_FOR_CLASSIFICATION.md)
## Contact Information
For inquiries, please email: [ottenbreitdatascience@gmail.com](mailto:ottenbreitdatascience@gmail.com)