Automatic Piecewise Linear Regression
Project description
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. 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.
Installation
To install APLR, use the following command:
pip install aplr
Availability
APLR is available for Windows, most Linux distributions, and macOS.
Usage
Example Python scripts are available here.
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
Contact Information
For inquiries, please email: ottenbreitdatascience@gmail.com
Project details
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MD5 | 16822aaeb63201dc1d26b748efce1edf |
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MD5 | a9bad82ffcdc3cfd5f512fa6aeb24fbd |
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Algorithm | Hash digest | |
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MD5 | 480e22d9205f97b4f75320ca89a7e602 |
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MD5 | a47e62af2e79177dd05c15471082e9aa |
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Hashes for aplr-10.7.3-cp310-cp310-win32.whl
Algorithm | Hash digest | |
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MD5 | d7875c27afe9893e6f08ed50500b97a5 |
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MD5 | e47f22f067ff0330f373cf5cbfa1e3b2 |
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MD5 | a3e8e149309b71951671e5b68a19b614 |
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MD5 | 75d4ee5c4750c1d2e7e821885b17edc9 |
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MD5 | e7bad9007c1eaff1c810c1d3355389a3 |
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MD5 | 846b04ed26a39d3d9fd51537868c9ac8 |
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Hashes for aplr-10.7.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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MD5 | 9f1e1796500cb30d7b1f087a8259f45c |
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Hashes for aplr-10.7.3-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
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MD5 | b6a286335174903db4490983787f3552 |
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Hashes for aplr-10.7.3-cp39-cp39-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
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MD5 | 0417e808588a54074bda30022a3218dc |
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Hashes for aplr-10.7.3-cp39-cp39-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
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MD5 | 4a62f2509da673252724b0cbfb0cd6f5 |
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Hashes for aplr-10.7.3-cp38-cp38-win_amd64.whl
Algorithm | Hash digest | |
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MD5 | 10042c65959bc5f52271afd6c292ae48 |
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BLAKE2b-256 | 2bb7d3aef1b2dc4b850a4ffbfd8a48368bdd75a0e9e37b9e4e10ec0f2c851035 |
Hashes for aplr-10.7.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
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MD5 | da8cf738170c6e8726c7e7cb39315176 |
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Hashes for aplr-10.7.3-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
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SHA256 | b097b8a9f45457e7db6e6778cb38f2decedb0ba20c2daf0233d2971a122cfe06 |
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MD5 | 4ba0f35c1264392732316311e56ce4c8 |
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BLAKE2b-256 | a7f8ecb07cc8636c0d2ecf73466e12551f9e6bd53171b6672bebaf720647a31c |
Hashes for aplr-10.7.3-cp38-cp38-macosx_11_0_arm64.whl
Algorithm | Hash digest | |
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SHA256 | 076dcfc25347e58db216fb73be91557e5e4b9f8e303787452ffeb6a413a6317b |
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MD5 | fbf502c494e68359d25911b0ef857ab5 |
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BLAKE2b-256 | 55edfe54c518620e17e23a1849ea683531af10da3a349e0495bda337dbbdf5a3 |
Hashes for aplr-10.7.3-cp38-cp38-macosx_10_14_x86_64.whl
Algorithm | Hash digest | |
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