{"id":15063990,"url":"https://github.com/omadson/scikit-mlm","last_synced_at":"2025-06-27T13:39:09.929Z","repository":{"id":34872781,"uuid":"150315610","full_name":"omadson/scikit-mlm","owner":"omadson","description":"Minimal Learning Machine implementation using the scikit-learn API. 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you may need to use the `--user` flag for the commands above to install in a non-system location (depends on your environment). alternatively, you can execute the `pip` commands with `sudo` (not recommended).\n - you may need to add the `--use-wheel` option if you have an older `pip` version (wheels are now the default binary package format for `pip`).\n\n\n## basic usage\nexample of classification with the [nearest neighbor MLM](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec9) classifier:\n```Python\nfrom skmlm import NN_MLM\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.datasets import load_iris\n\n# load dataset\ndataset = load_iris()\n\nclf = make_pipeline(MinMaxScaler(), NN_MLM(rp_number=20))\nscores = cross_val_score(clf, dataset.data, dataset.target, cv=10, scoring='accuracy')\n\nprint('AVG = %.3f, STD = %.3f' % (scores.mean(), scores.std()))\n```\n\n## how to cite scikit-mlm\nif you use `scikit-mlm` in your paper, please cite it in your publication.\n```\n@misc{scikit-mlm,\n    author       = \"Madson Luiz Dantas Dias\",\n    year         = \"2019\",\n    title        = \"scikit-mlm: An implementation of {MLM} for scikit-learn framework\",\n    url          = \"https://github.com/omadson/scikit-mlm\",\n    doi          = \"10.5281/zenodo.2875802\",\n    institution  = \"Federal University of Cear\\'{a}, Department of Computer Science\" \n}\n```\n\n## contributing\n\nthis project is open for contributions. here are some of the ways for you to contribute:\n - bug reports/fix\n - features requests\n - use-case demonstrations\n\nto make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a pull request!\n\n## list of implemented technics\n - [original regression (MLMR)](https://doi.org/10.1016/j.neucom.2014.11.073)\n - [original classification (MLMC)](https://doi.org/10.1016/j.neucom.2014.11.073)\n - [nearest neighbor MLM (NN_MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec9)\n - [opposite neighborhood MLM (ON_MLM)](https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2018-198.pdf)\n - [fuzzy C-means MLM (FCM_MLM)](https://doi.org/10.1007/978-3-319-95312-0_34)\n - [optimally selected MLM (OS_MLM)](https://doi.org/10.1007/978-3-030-03493-1_70)\n - [\u0026ell;\u003csub\u003e1/2\u003c/sub\u003e-norm regularization MLM (L12_MLM)](https://doi.org/10.1109/BRACIS.2018.00043)\n - [weighted MLM (w_MLM)](https://doi.org/10.1007/978-3-319-26532-2_61)\n - [ranking MLM (R_MLM)](https://doi.org/10.1109/BRACIS.2015.39) (WIP)\n - [cubic equation MLM (C_MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec10)\n\n\n\n## future improvements\n\nlist of methods that will be implemented in the next releases:\n - [expected squared distance MLM (ESD-MLM)](https://doi.org/10.1007/978-3-319-26532-2_62)\n - [voting based MLM (V-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)\n - [weighted voting based MLM (WV-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)\n - [random sampling voting based MLM (RSV-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)\n - [random sampling weighted voting based MLM (RSWV-MLM)](https://link.springer.com/article/10.1007%2Fs11063-017-9587-5#Sec11)\n - [reject option MLM (renjo-MLM)](https://doi.org/10.1109/BRACIS.2016.078)\n - [reject option weighted MLM (renjo-wMLM)](https://doi.org/10.1109/BRACIS.2016.078)\n \n\n\u003c!-- #### regression\n - [ ] [regularized M-FOCUSS MLM (RMF_MLM)]() --\u003e\n\n\u003c!-- ### speed up\n### missing values\n### ensemble \n### reject option\n### ranking --\u003e\n\n## contributors\n - [Madson Dias](https://github.com/omadson)\n\n## acknowledgement\n - thanks for [@JamesRitchie](https://github.com/JamesRitchie), the initial idea of this project is inspired on the [scikit-rvm](https://github.com/JamesRitchie/scikit-rvm) repo\n\n\n[1]: https://doi.org/10.1016/j.neucom.2014.11.073\n[2]: http://scikit-learn.org/\n[3]: https://doi.org/10.1007/s11063-017-9587-5#\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomadson%2Fscikit-mlm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fomadson%2Fscikit-mlm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fomadson%2Fscikit-mlm/lists"}