Scikit-learn

Authors: Jérémie du BoisberrangerJoris Van den BosscheLoïc EstèveThomas J. FanAlexandre GramfortOlivier GriselYaroslav HalchenkoNicolas HugAdrin JalaliJulien JerphanionGuillaume LemaitreChristian LorentzenJan Hendrik MetzenAndreas MuellerVlad NiculaeJoel NothmanHanmin QinBertrand ThirionTom Dupré la TourGael VaroquauxNelle VaroquauxRoman Yurchak
Updated: Tue 18 January 2022
Source: https://scikit-learn.org/stable/
Type: Python
Languages: N/A
Keywords: machine-learningPythonprogrammingclustering
Open Access: yes
License: BSD-3-Clause License
Documentation: https://scikit-learn.org/0.21/documentation.html
Publications: Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 12, 2825-2830.
Citation: Cournapeau, D. et al. (2007). Sci-kit-learn. https://scikit-learn.org/stable/index.html
Summary:

Scikit-learn: machine learning in Python - simple and efficient tools for predictive data analysis, accessible to everybody, and reusable in various contexts. It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.