https://github.com/scikit-learn-contrib/scikit-learn-contrib
scikit-learn compatible projects
https://github.com/scikit-learn-contrib/scikit-learn-contrib
Last synced: 2 months ago
JSON representation
scikit-learn compatible projects
- Host: GitHub
- URL: https://github.com/scikit-learn-contrib/scikit-learn-contrib
- Owner: scikit-learn-contrib
- Created: 2016-03-07T04:42:42.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2022-11-08T08:57:24.000Z (over 2 years ago)
- Last Synced: 2024-12-26T15:26:34.278Z (4 months ago)
- Homepage:
- Size: 25.4 KB
- Stars: 409
- Watchers: 45
- Forks: 50
- Open Issues: 19
-
Metadata Files:
- Readme: README.md
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README
# scikit-learn-contrib
scikit-learn-contrib is a github organization for gathering high-quality [scikit-learn](http://scikit-learn.org) compatible projects. It also provides a [template](https://github.com/scikit-learn-contrib/project-template) for establishing new scikit-learn compatible projects.
# Vision
With the explosion of the number of machine learning papers, it becomes increasingly difficult for users and researchers to implement and compare algorithms. Even when authors release their software, it takes time to learn how to use it and how to apply it to one's own purposes. The goal of scikit-learn-contrib is to provide **easy-to-install** and **easy-to-use** high-quality machine learning software. With scikit-learn-contrib, users can install a project by ``pip install sklearn-contrib-project-name`` and immediately try it on their data with the usual ``fit``, ``predict`` and ``transform`` methods. In addition, projects are compatible with scikit-learn tools such as grid search, pipelines, etc.
# Projects
If you would like to include your own project in scikit-learn-contrib,
take a look at the [workflow](https://github.com/scikit-learn-contrib/scikit-learn-contrib/blob/master/workflow.md).
## [DenMune: Density-peak clustering using mutual nearest neighbors](https://github.com/scikit-learn-contrib/denmune-clustering-algorithm)
A simple-but-efficient density-based clustering algorithm that can find clusters of arbitrary size, shapes and densities in two-dimensions. Higher dimensions are first reduced to 2-D using the t-sne. The algorithm relies on a single parameter K, the number of nearest neighbors.
[Read The Docs](https://denmune.readthedocs.io/en/latest/), [Read the Paper](https://doi.org/10.1016/j.patcog.2020.107589)
Maintained by: [Mohamed Abbas](https://github.com/egy1st)
## [lightning](http://contrib.scikit-learn.org/lightning/)
Large-scale linear classification, regression and ranking.
Maintained by [Mathieu Blondel](https://github.com/mblondel) and [Fabian Pedregosa](https://github.com/fabianp).
## [skglm](https://contrib.scikit-learn.org/skglm)
Fast and modular Generalized Linear Models with support for models missing in scikit-learn.
Maintained by [Mathurin Massias](https://github.com/mathurinm), [Pierre-Antoine Bannier](https://github.com/PABannier), [Quentin Klopfenstein](https://github.com/Klopfe) and [Quentin Bertrand](https://github.com/QB3).
## [py-earth](https://github.com/scikit-learn-contrib/py-earth)
A Python implementation of Jerome Friedman's Multivariate Adaptive Regression Splines.
Maintained by [Jason Rudy](https://github.com/jcrudy) and [Mehdi](https://github.com/mehdidc).
## [imbalanced-learn](https://github.com/scikit-learn-contrib/imbalanced-learn)
Python module to perform under sampling and over sampling with various techniques.
Maintained by [Guillaume Lemaitre](https://github.com/glemaitre), [Fernando Nogueira](https://github.com/fmfn), [Dayvid Oliveira](https://github.com/dvro) and [Christos Aridas](https://github.com/chkoar).
## [polylearn](https://github.com/scikit-learn-contrib/polylearn)
Factorization machines and polynomial networks for classification and regression in Python.
Maintained by [Vlad Niculae](https://github.com/vene).
## [forest-confidence-interval](https://github.com/scikit-learn-contrib/forest-confidence-interval)
Confidence intervals for scikit-learn forest algorithms.
Maintained by [Ariel Rokem](https://github.com/arokem), [Kivan Polimis](https://github.com/kpolimis) and [Bryna Hazelton](https://github.com/bhazelton).
## [hdbscan](http://hdbscan.readthedocs.io/en/latest/)
A high performance implementation of HDBSCAN clustering.
Maintained by [Leland McInnes](https://github.com/lmcinnes), [jc-healy](https://github.com/jc-healy), [c-north](https://github.com/c-north) and [Steve Astels](https://github.com/sastels).
## [categorical-encoding](http://github.com/scikit-learn-contrib/categorical-encoding)
A library of sklearn compatible categorical variable encoders.
Maintained by [Will McGinnis](https://github.com/wdm0006) and [Paul Westenthanner](https://github.com/PaulWestenthanner)
## [boruta_py](https://github.com/scikit-learn-contrib/boruta_py)
Python implementations of the Boruta all-relevant feature selection method.
Maintained by [Daniel Homola](https://github.com/danielhomola)
## [sklearn-pandas](https://github.com/scikit-learn-contrib/sklearn-pandas)
Pandas integration with sklearn.
Maintained by [Israel Saeta Pérez](https://github.com/dukebody)
## [skope-rules](https://github.com/scikit-learn-contrib/skope-rules)
Machine learning with logical rules in Python.
Maintained by [Florian Gardin](https://github.com/floriangardin), [Ronan Gautier](https://github.com/RonanGautier), [Nicolas Goix](https://github.com/ngoix) and [Jean-Matthieu Schertzer](https://github.com/datajms).
## [stability-selection](https://github.com/scikit-learn-contrib/stability-selection)
A Python implementation of the stability selection feature selection algorithm.
Maintained by [Thomas Huijskens](https://github.com/thuijskens)
## [metric-learn](https://github.com/scikit-learn-contrib/metric-learn)
Metric learning algorithms in Python.
Maintained by [CJ Carey](https://github.com/perimosocordiae), [Yuan Tang](https://github.com/terrytangyuan), [William de Vazelhes](https://github.com/wdevazelhes), [Aurélien Bellet](https://github.com/bellet) and [Nathalie Vauquier](https://github.com/nvauquie).