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 430 articles , 1 standard article )

Showing results 1 to 20 of 430.
Sorted by year (citations)

1 2 3 ... 20 21 22 next

  1. Adam Richie-Halford, Manjari Narayan, Noah Simon, Jason Yeatman, Ariel Rokem: Groupyr: Sparse Group Lasso in Python (2021) not zbMATH
  2. Alexander Fabisch: gmr: Gaussian Mixture Regression (2021) not zbMATH
  3. Ali Haidar, Matthew Field, Jonathan Sykes, Martin Carolan, Lois Holloway: PSPSO: A package for parameters selection using particle swarm optimization (2021) not zbMATH
  4. Ali, Mehdi; Berrendorf, Max; Hoyt, Charles Tapley; Vermue, Laurent; Sharifzadeh, Sahand; Tresp, Volker; Lehmann, Jens: PyKEEN 1.0: a Python library for training and evaluating knowledge graph embeddings (2021)
  5. Andrea Bommert, Michel Lang: stabm: Stability Measures for Feature Selection (2021) not zbMATH
  6. Anirudhan Badrinath, Frederic Wang, Zachary Pardos: pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models (2021) arXiv
  7. Antoine de Mathelin, François Deheeger, Guillaume Richard, Mathilde Mougeot, Nicolas Vayatis: ADAPT : Awesome Domain Adaptation Python Toolbox (2021) arXiv
  8. Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi: Ecole: A Library for Learning Inside MILP Solvers (2021) arXiv
  9. Barber, Rina Foygel; Candès, Emmanuel J.; Ramdas, Aaditya; Tibshirani, Ryan J.: Predictive inference with the jackknife+ (2021)
  10. 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)
  11. Benjamin Paaßen, Jessica McBroom, Bryn Jeffries, Irena Koprinska, Kalina Yacef: ast2vec: Utilizing Recursive Neural Encodings of Python Programs (2021) arXiv
  12. Chandan Singh; Keyan Nasseri; Yan Shuo Tan; Tiffany Tang; Bin Yu: imodels: a python package for fitting interpretable models (2021) not zbMATH
  13. Chen, Shunqin; Guo, Zhengfeng; Zhao, Xinlei: Predicting mortgage early delinquency with machine learning methods (2021)
  14. Christopher P. Bridge, Chris Gorman, Steven Pieper, Sean W. Doyle, Jochen K. Lennerz, Jayashree Kalpathy-Cramer, David A. Clunie, Andriy Y. Fedorov, Markus D. Herrmann: Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology (2021) arXiv
  15. D.C.L. Handler, P.A. Haynes: PeptideMind - Applying machine learning algorithms to assess replicate quality in shotgun proteomic data (2021) not zbMATH
  16. 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)
  17. Fermanian, Adeline: Embedding and learning with signatures (2021)
  18. Feurer, Matthias; van Rijn, Jan N.; Kadra, Arlind; Gijsbers, Pieter; Mallik, Neeratyoy; Ravi, Sahithya; Müller, Andreas; Vanschoren, Joaquin; Hutter, Frank: OpenML-Python: an extensible Python API for OpenML (2021)
  19. Gambella, Claudio; Ghaddar, Bissan; Naoum-Sawaya, Joe: Optimization problems for machine learning: a survey (2021)
  20. Gareth D. Simons: The cityseer Python package for pedestrian-scale network-based urban analysis (2021) arXiv

1 2 3 ... 20 21 22 next