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 268 articles , 1 standard article )

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  1. Andrew Dawson: eofs: A Library for EOF Analysis of Meteorological, Oceanographic, and Climate Data (2016) not zbMATH
  2. Bayer, Immanuel: fastFM: a library for factorization machines (2016)
  3. Bischl, Bernd; Lang, Michel; Kotthoff, Lars; Schiffner, Julia; Richter, Jakob; Studerus, Erich; Casalicchio, Giuseppe; Jones, Zachary M.: mlr: machine learning in (\mathbfR) (2016)
  4. Bouhlel, Mohamed Amine; Bartoli, Nathalie; Otsmane, Abdelkader; Morlier, Joseph: An improved approach for estimating the hyperparameters of the Kriging model for high-dimensional problems through the partial least squares method (2016)
  5. Chandar, Sarath; Khapra, Mitesh M.; Larochelle, Hugo; Ravindran, Balaraman: Correlational neural networks (2016)
  6. Clémençon, Stephan; Colin, Igor; Bellet, Aurélien: Scaling-up empirical risk minimization: optimization of incomplete (U)-statistics (2016)
  7. Eiter, Thomas; Kaminski, Tobias: Exploiting contextual knowledge for hybrid classification of visual objects (2016)
  8. Er, Meng Joo; Zhang, Yong; Wang, Ning; Pratama, Mahardhika: Attention pooling-based convolutional neural network for sentence modelling (2016)
  9. 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)
  10. Hottung, André; Tierney, Kevin: A biased random-key genetic algorithm for the container pre-marshalling problem (2016)
  11. Igino Corona, Battista Biggio, Davide Maiorca: AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack (2016) arXiv
  12. Jake VanderPlas: mst_clustering: Clustering via Euclidean Minimum Spanning Trees (2016) not zbMATH
  13. Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic: TensorLy: Tensor Learning in Python (2016) arXiv
  14. Jessup, Elizabeth; Motter, Pate; Norris, Boyana; Sood, Kanika: Performance-based numerical solver selection in the Lighthouse framework (2016)
  15. Kramer, Oliver: Machine learning for evolution strategies (2016)
  16. Ling, Julia; Jones, Reese; Templeton, Jeremy: Machine learning strategies for systems with invariance properties (2016)
  17. Mario Mulansky, Thomas Kreuz: PySpike - A Python library for analyzing spike train synchrony (2016) arXiv
  18. McQueen, James; Meilă, Marina; VanderPlas, Jacob; Zhang, Zhongyue: Megaman: scalable manifold learning in Python (2016)
  19. 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)
  20. Park, Young Woong; Klabjan, Diego: An aggregate and iterative disaggregate algorithm with proven optimality in machine learning (2016)

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