MateriApps
A Portal Site of Materials Science Simulation

now 326 Apps

Inquiry / Application Request
Detailed search

scikit-learn

  • Openness:3 ★★★
  • Document quality:3 ★★★

An open-source library for data mining and data analysis. This package implements various methods of machine learning such as supervised learning (data classification, data regression, etc.), unsupervised learning (data clustering, etc.), and data pre-processing. This package is implemented on Python numerical libraries, NumPy and Scipy, and supports parallel computation.

Information
Last Update:2024/10/10

Official site

https://scikit-learn.org/stable/index.html

License

BSD license

Availability

Unix/Linux (Environment in which Python can be used.)
Core Developers

Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, and other many contributors (see this page)
Target substance/model

Data classification, data regression, clustering
Methodology

Machine learning, data mining, data analysis
Parallelization

Parallel computation is supported. The number of parallel jobs can be described by a parameter.
Related keywords
Document
  1. F. Pedregosa, G. Varoquaux, A. Gramfort, B. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Journal of Machine Learning Research 12, 2825 (2011).
  2. L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt and G. Varoquaux, ECML PKDD Workshop: Languages for Data Mining and Machine Learning p.108 (2013).
Other

Implemented on Python numerical libraries, Numpy and SciPy. Some core algorithms, which needs fast operation, are written by Cython. BLAS and LAPACK are needed.
TOP