Automatic Piecewise Linear Regression

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Project description

APLR

Automatic Piecewise Linear Regression.

About

Build predictive and interpretable parametric regression or classification machine learning models in Python based on the Automatic Piecewise Linear Regression (APLR) methodology developed by Mathias von Ottenbreit. APLR is often able to compete with tree-based methods on predictiveness, but unlike tree-based methods APLR is interpretable. Please see the documentation for more information. Links to published article: https://link.springer.com/article/10.1007/s00180-024-01475-4 and https://rdcu.be/dz7bF. More functionality has been added to APLR since the article was published.

How to install

pip install aplr

Availability

Available for Windows, most Linux distributions and MacOS.

How to use

Please see the two example Python scripts here. They cover common use cases, but not all of the functionality in this package.

Sponsorship

Please consider sponsoring Ottenbreit Data Science by clicking on the Sponsor button. Sufficient funding will enable maintenance of APLR and further development.

API reference

Please see the API reference for regression and API reference for classification.

Project details

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Release history Release notifications | RSS feed

10.7.3

10.7.2

10.7.1

10.7.0

10.6.4

10.6.3

10.6.2

10.6.1

10.6.0

10.5.1

10.5.0

This version

10.4.6

10.4.5

10.4.4

10.4.3

10.4.2

10.4.1

10.4.0

10.3.0

10.2.1

10.2.0

10.1.0

10.0.0

9.10.1

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9.8.0

9.7.0

9.6.0

9.5.0

9.4.3

9.4.2

9.4.1

9.4.0

9.3.1 yanked

Reason this release was yanked:

Bug

9.3.0

9.2.4

9.2.3

9.2.2

9.2.1

9.2.0

9.1.0

9.0.0

8.1.0 yanked

Reason this release was yanked:

The functionality added in 8.1.0 does not seem to add predictive value (based on empirical tests). However, it works from a technical point of view though.

8.0.0

7.8.1

7.8.0

7.7.0

7.6.2

7.6.1

7.6.0

7.5.1

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1.10.1

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1.9.0

1.8.5

1.8.4

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1.8.1

1.8.0

1.7.1

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1.6.2

1.6.1

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1.4.1 yanked

1.4.0 yanked

1.3.2

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1.1.1

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1.0.9

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1.0.7

1.0.6

1.0.5

1.0.4

1.0.3

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