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

Release history Release notifications | RSS feed

This version

10.7.4

Nov 13, 2024

10.7.3

Nov 5, 2024

10.7.2

Nov 2, 2024

10.7.1

Oct 29, 2024

10.7.0

Oct 29, 2024

10.6.4

Oct 17, 2024

10.6.3

Sep 5, 2024

10.6.2

Aug 25, 2024

10.6.1

Aug 8, 2024

10.6.0

Aug 3, 2024

10.5.1

Jul 7, 2024

10.5.0

Jul 6, 2024

10.4.6

Jun 24, 2024

10.4.5

Jun 22, 2024

10.4.4

Jun 20, 2024

10.4.3

Jun 12, 2024

10.4.2

Jun 10, 2024

10.4.1

Jun 9, 2024

10.4.0

Jun 8, 2024

10.3.0

Jun 6, 2024

10.2.1

May 27, 2024

10.2.0

May 26, 2024

10.1.0

May 25, 2024

10.0.0

May 23, 2024

9.10.1

May 22, 2024

9.10.0

May 21, 2024

9.9.0

May 15, 2024

9.8.0

Apr 25, 2024

9.7.0

Apr 22, 2024

9.6.0

Apr 21, 2024

9.5.0

Apr 13, 2024

9.4.3

Apr 11, 2024

9.4.2

Mar 14, 2024

9.4.1

Mar 8, 2024

9.4.0

Mar 7, 2024

9.3.1 yanked

Mar 7, 2024

Reason this release was yanked:

Bug

9.3.0

Mar 1, 2024

9.2.4

Feb 29, 2024

9.2.3

Feb 25, 2024

9.2.2

Feb 22, 2024

9.2.1

Feb 19, 2024

9.2.0

Feb 17, 2024

9.1.0

Jan 21, 2024

9.0.0

Jan 21, 2024

8.1.0 yanked

Jan 10, 2024

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

Jan 3, 2024

7.8.1

Dec 14, 2023

7.8.0

Nov 27, 2023

7.7.0

Nov 26, 2023

7.6.2

Nov 25, 2023

7.6.1

Nov 19, 2023

7.6.0

Nov 15, 2023

7.5.1

Oct 19, 2023

7.5.0

Oct 15, 2023

7.4.1

Oct 13, 2023

7.4.0

Oct 12, 2023

7.3.0

Oct 9, 2023

7.2.0

Sep 29, 2023

7.1.0

Sep 26, 2023

7.0.1

Sep 25, 2023

7.0.0

Sep 24, 2023

6.5.2

Jul 22, 2023

6.5.1

Jul 21, 2023

6.5.0

Jul 14, 2023

6.4.0

Jul 4, 2023

6.3.0

Jul 3, 2023

6.2.0

Jul 1, 2023

6.1.0

Jun 30, 2023

6.0.0

Jun 29, 2023

5.0.0

Jun 28, 2023

4.1.0

Jun 27, 2023

4.0.0

May 24, 2023

3.1.0

May 6, 2023

3.0.0

May 5, 2023

2.7.0

May 4, 2023

2.6.0

May 2, 2023

2.5.0

Apr 23, 2023

2.4.0

Apr 23, 2023

2.3.0

Apr 19, 2023

2.2.0

Apr 4, 2023

2.1.0

Mar 28, 2023

2.0.1

Mar 24, 2023

2.0.0

Mar 22, 2023

1.12.1

Mar 15, 2023

1.12.0

Mar 15, 2023

1.11.0

Feb 22, 2023

1.10.2

Feb 18, 2023

1.10.1

Feb 16, 2023

1.10.0

Feb 14, 2023

1.9.0

Jan 31, 2023

1.8.5

Jan 10, 2023

1.8.4

Jan 6, 2023

1.8.3

Jan 6, 2023

1.8.2

Jan 5, 2023

1.8.1

Jan 5, 2023

1.8.0

Jan 3, 2023

1.7.1

Dec 24, 2022

1.7.0

Dec 22, 2022

1.6.2

Dec 22, 2022

1.6.1

Dec 7, 2022

1.6.0

Dec 6, 2022

1.5.0

Dec 2, 2022

1.4.1 yanked

Nov 30, 2022

1.4.0 yanked

Nov 30, 2022

1.3.2

Nov 21, 2022

1.3.1

Nov 18, 2022

1.3.0

Oct 21, 2022

1.2.2

Oct 20, 2022

1.2.1

Oct 19, 2022

1.2.0

Oct 14, 2022

1.1.2

Oct 13, 2022

1.1.1

Oct 12, 2022

1.1.0

Oct 11, 2022

1.0.9

Jun 25, 2022

1.0.8

Jun 17, 2022

1.0.7

Jun 16, 2022

1.0.6

Jun 15, 2022

1.0.5

Jun 15, 2022

1.0.4

Jun 15, 2022

1.0.3

May 23, 2022

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aplr 10.7.4

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Algorithm	Hash digest
SHA256	`e4d654a5b84f2d448bcb3bfe60ea8828ebd6431571b750ba915f34d34eca4c02`
MD5	`82e74354faa45b5a4624172766c002cb`
BLAKE2b-256	`0b62cf97d3318971af02f9a686930368e72a56122d7d4fb41e2d2566296d1763`

Algorithm	Hash digest
SHA256	`8e71f50603a253044d2c03fafa4d4807270bc7e1348af7ac06423b49d73c5c73`
MD5	`21ac5c767a43391d8dda07ed5ac2f449`
BLAKE2b-256	`fee44fe7650772ffe6e3e8a87a4efd714144c4c7896ca6aeb3098172bdbcd5f4`

Algorithm	Hash digest
SHA256	`a7054c3cd4211af83f6b8103f247257eb4d642abeda332515a8fd845ce503121`
MD5	`0dfadf59849e24feedf1e01be25586c3`
BLAKE2b-256	`a5023c6d1f10e2fe6c08f26262051b12e7b822e704b5116101a6f8487a51a523`

Algorithm	Hash digest
SHA256	`ae96ca1e4355650adbe04327dccdb91a83b97e56ad1695596b89d955e3131592`
MD5	`6ac054ce07173cd17fd40c3733147f1b`
BLAKE2b-256	`d1c860a3ba32690c1277192adfbaae8ef370d0638706f0b47127b3b93a18a5e3`

Algorithm	Hash digest
SHA256	`ab6145147bcff49addc1f1ead2084363086a644dc413dd41e9b65a36826f70dd`
MD5	`f9f6c0bb1267c95aa7b0ff92beffecaa`
BLAKE2b-256	`562a518db278f4ce9daca51aa79aed06eca727fb6a8b1c853c0dc122db316f80`

Algorithm	Hash digest
SHA256	`108bf096161c251d62ec694fdbfc67d9a3b586c79be213906ea6c470a3fd001c`
MD5	`982d826cbf4b66270a457179d074c1d4`
BLAKE2b-256	`00629bbd10d38bc4b652304cb6550a69e73ed09428f35dd459a784f246cb5749`

Algorithm	Hash digest
SHA256	`0e3d402b655653fc51402f02d765dc3fdbf06e9e68ff066061537f893172413e`
MD5	`031bd0dc52046b0382e7a14d1a5d584b`
BLAKE2b-256	`08d9ccc967312783ee2d99645ed644a5fe82c58d46e8405a92e6696459bd5079`

Algorithm	Hash digest
SHA256	`4d688675be6a2e684547e3944171e4c76eb7eff80d55f0db088b8ac12140dc22`
MD5	`e9465066f128e40d344f44c566e0a510`
BLAKE2b-256	`007757005be114b343e8652917dcbb837f70dc18d83f28802a50fe86b6ee3b97`

Algorithm	Hash digest
SHA256	`468689ce986feeb9450eedffb4fda015c985251200fd84b2c9e762e1e0343c2d`
MD5	`f10059b8f247a280e26fd530cde4b04e`
BLAKE2b-256	`2fe263ce4d2ce757eb237a107080a8538ac411cfaffaca855f2980d1dcfe354c`

Algorithm	Hash digest
SHA256	`49a73c0d44eb2210b4bcc35f16fcbd4d295191b9f09fe3e8a267544cb6abf25c`
MD5	`d64b9d7effe2ceee4f1dbabff9e52050`
BLAKE2b-256	`e563cbd174378a9acfda8da58e359191577a1ad0d813677663d9eb80c29d61de`