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https://github.com/jhumphry/regressions
A Python package that implements various regression algorithms, including Partial Least Squares and Principal Components Regression
https://github.com/jhumphry/regressions
Last synced: 3 months ago
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A Python package that implements various regression algorithms, including Partial Least Squares and Principal Components Regression
- Host: GitHub
- URL: https://github.com/jhumphry/regressions
- Owner: jhumphry
- License: isc
- Created: 2015-11-08T21:52:07.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2023-03-01T21:49:56.000Z (over 1 year ago)
- Last Synced: 2024-07-29T07:31:52.806Z (4 months ago)
- Language: Python
- Homepage:
- Size: 72.3 KB
- Stars: 22
- Watchers: 2
- Forks: 18
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Regressions
This package provides various forms of regression. The aim of these modules is
to achieve clarity of implementation with a clear connection to the
mathematical descriptions of the algorithms. The motivation for creating the
package was the desire to learn about and explore the use of Principal
Components Regression, Partial Least Squares regression and non-linear
kernel-based Partial Least Squares regression.Python 3.5 and Numpy 1.10 or greater are required as the new '@' matrix
multiplication operator is used. If SciPy is available some linear algebra
routines may be used as they can sometimes be faster than the routines in
Numpy - however SciPy is not required. Matplotlib is used by the examples to
display the results.Full documentation of the API is maintained using Sphinx - see the `doc`
directory.