Scikit

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{http://scikit-learn.sourceforge.net}.


References in zbMATH (referenced in 392 articles , 1 standard article )

Showing results 1 to 20 of 392.
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  1. Adam Richie-Halford, Manjari Narayan, Noah Simon, Jason Yeatman, Ariel Rokem: Groupyr: Sparse Group Lasso in Python (2021) not zbMATH
  2. Andrea Bommert, Michel Lang: stabm: Stability Measures for Feature Selection (2021) not zbMATH
  3. Anirudhan Badrinath, Frederic Wang, Zachary Pardos: pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models (2021) arXiv
  4. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  5. Barber, Rina Foygel; Candès, Emmanuel J.; Ramdas, Aaditya; Tibshirani, Ryan J.: Predictive inference with the jackknife+ (2021)
  6. Bartzos, Evangelos; Emiris, Ioannis Z.; Legerský, Jan; Tsigaridas, Elias: On the maximal number of real embeddings of minimally rigid graphs in (\mathbbR^2,\mathbbR^3) and (S^2) (2021)
  7. Benjamin Paaßen, Jessica McBroom, Bryn Jeffries, Irena Koprinska, Kalina Yacef: ast2vec: Utilizing Recursive Neural Encodings of Python Programs (2021) arXiv
  8. Chandan Singh; Keyan Nasseri; Yan Shuo Tan; Tiffany Tang; Bin Yu: imodels: a python package for fitting interpretable models (2021) not zbMATH
  9. D.C.L. Handler, P.A. Haynes: PeptideMind - Applying machine learning algorithms to assess replicate quality in shotgun proteomic data (2021) not zbMATH
  10. Ding, Chenchen; Han, Haitao; Li, Qianyue; Yang, Xiaoxia; Liu, Taigang: iT3SE-PX: identification of bacterial type III secreted effectors using PSSM profiles and XGBoost feature selection (2021)
  11. Fermanian, Adeline: Embedding and learning with signatures (2021)
  12. Guo, Liang; Liu, Jianya; Lu, Ruodan: Subsampling bias and the best-discrepancy systematic cross validation (2021)
  13. Hitoshi Manabe, Masato Hagiwara: EXPATS: A Toolkit for Explainable Automated Text Scoring (2021) arXiv
  14. Karban, Pavel; Pánek, David; Orosz, Tamás; Petrášová, Iveta; Doležel, Ivo: FEM based robust design optimization with Agros and Ārtap (2021)
  15. Li, Rui; Reich, Brian J.; Bondell, Howard D.: Deep distribution regression (2021)
  16. Mudunuru, M. K.; Karra, S.: Physics-informed machine learning models for predicting the progress of reactive-mixing (2021)
  17. Peiris, V.; Sharon, N.; Sukhorukova, N.; Ugon, J.: Generalised rational approximation and its application to improve deep learning classifiers (2021)
  18. Petri Laarne, Martha A. Zaidan, Tuomo Nieminen: ennemi: Non-linear correlation detection with mutual information (2021) not zbMATH
  19. Platzer, Auriane; Leygue, Adrien; Stainier, Laurent; Ortiz, Michael: Finite element solver for data-driven finite strain elasticity (2021)
  20. Read, Jesse; Pfahringer, Bernhard; Holmes, Geoffrey; Frank, Eibe: Classifier chains: a review and perspectives (2021)

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