Scikit-learn: machine learning in python. Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from url{}.

References in zbMATH (referenced in 16 articles , 1 standard article )

Showing results 1 to 16 of 16.
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  1. Xie, Jiang; Xu, Junfu; Nie, Celine; Nie, Qing: Machine learning of swimming data via wisdom of crowd and regression analysis (2017)
  2. Frandi, Emanuele; Ñanculef, Ricardo; Lodi, Stefano; Sartori, Claudio; Suykens, Johan A.K.: Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee (2016)
  3. Jessup, Elizabeth; Motter, Pate; Norris, Boyana; Sood, Kanika: Performance-based numerical solver selection in the lighthouse framework (2016)
  4. Meng, Xiangrui; Bradley, Joseph; Yavuz, Burak; Sparks, Evan; Venkataraman, Shivaram; Liu, Davies; Freeman, Jeremy; Tsai, Db; Amde, Manish; Owen, Sean; Xin, Doris; Xin, Reynold; Franklin, Michael J.; Zadeh, Reza; Zaharia, Matei; Talwalkar, Ameet: MLlib: machine learning in apache spark (2016)
  5. Geist, Matthieu: Soft-max boosting (2015)
  6. Germain, Pascal; Lacasse, Alexandre; Laviolette, Francois; Marchand, Mario; Roy, Jean-Francis: Risk bounds for the majority vote: from a PAC-Bayesian analysis to a learning algorithm (2015)
  7. Li, Chun-Liang; Su, Yu-Chuan; Lin, Ting-Wei; Tsai, Cheng-Hao; Chang, Wei-Cheng; Huang, Kuan-Hao; Kuo, Tzu-Ming; Lin, Shan-Wei; Lin, Young-San; Lu, Yu-Chen; Yang, Chun-Pai; Chang, Cheng-Xia; Chin, Wei-Sheng; Juan, Yu-Chin; Tung, Hsiao-Yu; Wang, Jui-Pin; Wei, Cheng-Kuang; Wu, Felix; Yin, Tu-Chun; Yu, Tong; Zhuang, Yong; Lin, Shou-De; Lin, Hsuan-Tien; Lin, Chih-Jen: Combination of feature engineering and ranking models for paper-author identification in KDD cup 2013 (2015)
  8. Neumann, Marion; Huang, Shan; Marthaler, Daniel E.; Kersting, Kristian: pyGPS -- a python library for Gaussian process regression and classification (2015)
  9. Swaminathan, Adith; Joachims, Thorsten: Batch learning from logged bandit feedback through counterfactual risk minimization (2015)
  10. Vejdemo-Johansson, Mikael; Pokorny, Florian T.; Skraba, Primoz; Kragic, Danica: Cohomological learning of periodic motion (2015)
  11. Barbuti, Roberto; Maggiolo-Schettini, Andrea; Milazzo, Paolo; Pardini, Giovanni: Simulation of spatial P system models (2014)
  12. Cobo, Luis C.; Subramanian, Kaushik; Isbell, Charles L.jun.; Lanterman, Aaron D.; Thomaz, Andrea L.: Abstraction from demonstration for efficient reinforcement learning in high-dimensional domains (2014)
  13. Müller, Andreas C.; Behnke, Sven: Pystruct-learning structured prediction in Python (2014)
  14. Moewes, Christian; Kruse, Rudolf; Sabel, Bernhard A.: Analysis of dynamic brain networks using VAR models (2013)
  15. Michel, Vincent; Gramfort, Alexandre; Varoquaux, Gaël; Eger, Evelyn; Keribin, Christine; Thirion, Bertrand: A supervised clustering approach for fMRI-based inference of brain states (2012)
  16. Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu; Duchesnay, Édouard: Scikit-learn: machine learning in Python (2011)