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 640 articles , 1 standard article )

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  1. Ah-Pine, Julien: Learning doubly stochastic and nearly idempotent affinity matrix for graph-based clustering (2022)
  2. Aldroubi, Akram; Diaz Martin, Rocio; Medri, Ivan; Rohde, Gustavo K.; Thareja, Sumati: The signed cumulative distribution transform for 1-D signal analysis and classification (2022)
  3. Alina Petukhova, Nuno Fachada: TextCL: A Python package for NLP preprocessing tasks (2022) not zbMATH
  4. Al-Mekhlafi, Amani; Becker, Tobias; Klawonn, Frank: Sample size and performance estimation for biomarker combinations based on pilot studies with small sample sizes (2022)
  5. Alvaro J. Garcia-Tejedor, Alberto Nogales: GEMA: An open-source Python library for self-organizing-maps (2022) arXiv
  6. Archibald, Richard; Tran, Hoang: A dictionary learning algorithm for compression and reconstruction of streaming data in preset order (2022)
  7. Askari, Armin; d’Aspremont, Alexandre; El Ghaoui, Laurent: Approximation bounds for sparse programs (2022)
  8. Bajaria, Pratik; Yerudkar, Amol; Glielmo, Luigi; Del Vecchio, Carmen; Wu, Yuhu: Self-triggered control of probabilistic Boolean control networks: a reinforcement learning approach (2022)
  9. Belli, Edoardo: Smoothly adaptively centered ridge estimator (2022)
  10. Bergman, David; Huang, Teng; Brooks, Philip; Lodi, Andrea; Raghunathan, Arvind U.: JANOS: an integrated predictive and prescriptive modeling framework (2022)
  11. Bertsimas, Dimitris; Digalakis, Vassilis jun.: The backbone method for ultra-high dimensional sparse machine learning (2022)
  12. Bouchnita, Anass; Nony, Patrice; Llored, Jean-Pierre; Volpert, Vitaly: Combining mathematical modeling and deep learning to make rapid and explainable predictions of the patient-specific response to anticoagulant therapy under venous flow (2022)
  13. Boudabsa, Lotfi; Filipović, Damir: Machine learning with kernels for portfolio valuation and risk management (2022)
  14. Brunet-Saumard, Camille; Genetay, Edouard; Saumard, Adrien: K-bMOM: A robust Lloyd-type clustering algorithm based on bootstrap median-of-means (2022)
  15. Caton, Simon; Malisetty, Saiteja; Haas, Christian: Impact of imputation strategies on fairness in machine learning (2022)
  16. Chandna, Akshat; Srinivasan, Sanjay: Mapping natural fracture networks using geomechanical inferences from machine learning approaches (2022)
  17. Chou, Ping; Chuang, Howard Hao-Chun; Chou, Yen-Chun; Liang, Ting-Peng: Predictive analytics for customer repurchase: interdisciplinary integration of buy till you die modeling and machine learning (2022)
  18. Chung, Yu-Min; Lawson, Austin: Persistence curves: a canonical framework for summarizing persistence diagrams (2022)
  19. Coma-Puig, Bernat; Carmona, Josep: Non-technical losses detection in energy consumption focusing on energy recovery and explainability (2022)
  20. Cowen-Rivers, Alexander I.; Lyu, Wenlong; Tutunov, Rasul; Wang, Zhi; Grosnit, Antoine; Griffiths, Ryan Rhys; Maraval, Alexandre Max; Jianye, Hao; Wang, Jun; Peters, Jan; Bou-Ammar, Haitham: \textttHEBO: Pushing the limits of sample-efficient hyper-parameter optimisation (2022)

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