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 278 articles , 1 standard article )

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  1. Igino Corona, Battista Biggio, Davide Maiorca: AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack (2016) arXiv
  2. Jake VanderPlas: mst_clustering: Clustering via Euclidean Minimum Spanning Trees (2016) not zbMATH
  3. Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic: TensorLy: Tensor Learning in Python (2016) arXiv
  4. Jessup, Elizabeth; Motter, Pate; Norris, Boyana; Sood, Kanika: Performance-based numerical solver selection in the Lighthouse framework (2016)
  5. Kramer, Oliver: Machine learning for evolution strategies (2016)
  6. Ling, Julia; Jones, Reese; Templeton, Jeremy: Machine learning strategies for systems with invariance properties (2016)
  7. Mario Mulansky, Thomas Kreuz: PySpike - A Python library for analyzing spike train synchrony (2016) arXiv
  8. McQueen, James; Meilă, Marina; VanderPlas, Jacob; Zhang, Zhongyue: Megaman: scalable manifold learning in Python (2016)
  9. 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)
  10. Park, Young Woong; Klabjan, Diego: An aggregate and iterative disaggregate algorithm with proven optimality in machine learning (2016)
  11. Robert T. McGibbon; Carlos X. Hernandez; Matthew P. Harrigan; Steven Kearnes; Mohammad M. Sultan; Stanislaw Jastrzebski; Brooke E. Husic; Vijay S. Pande: Osprey: Hyperparameter Optimization for Machine Learning (2016) not zbMATH
  12. Thomas Keck: FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification (2016) arXiv
  13. Tianqi Chen, Carlos Guestrin: XGBoost: A Scalable Tree Boosting System (2016) arXiv
  14. Unpingco, José: Python for probability, statistics, and machine learning (2016)
  15. Wieczorek, Wojciech; Unold, Olgierd: Use of a novel grammatical inference approach in classification of amyloidogenic hexapeptides (2016)
  16. Zhao, Shiwen; Gao, Chuan; Mukherjee, Sayan; Engelhardt, Barbara E.: Bayesian group factor analysis with structured sparsity (2016)
  17. Dae-Won Kim, Coryn A.L. Bailer-Jones: A Package for the Automated Classification of Periodic Variable Stars (2015) arXiv
  18. Fernández-Martínez, M.; Sánchez-Granero, M. A.: How to calculate the Hausdorff dimension using fractal structures (2015)
  19. Geist, Matthieu: Soft-max boosting (2015)
  20. 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)

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