Orange: data mining toolbox in Python. Orange is a machine learning and data mining suite for data analysis through Python scripting and visual programming. Here we report on the scripting part, which features interactive data analysis and component-based assembly of data mining procedures. In the selection and design of components, we focus on the flexibility of their reuse: our principal intention is to let the user write simple and clear scripts in Python, which build upon C$++$ implementations of computationally-intensive tasks. Orange is intended both for experienced users and programmers, as well as for students of data mining.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 19 articles , 2 standard articles )

Showing results 1 to 19 of 19.
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  1. Stefanovič, Pavel; Kurasova, Olga: Approach for multi-label text data class verification and adjustment based on self-organizing map and latent semantic analysis (2022)
  2. Ignatiev, Alexey; Marques-Silva, Joao: SAT-based rigorous explanations for decision lists (2021)
  3. Tomaž Hočevar, Blaž Zupan, Jonna Stålring: Conformal Prediction with Orange (2021) not zbMATH
  4. Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
  5. Adam Gudyś, Marek Sikora, Łukasz Wróbel: RuleKit: A Comprehensive Suite for Rule-Based Learning (2019) arXiv
  6. Dzemyda, Gintautas; Kurasova, Olga; Medvedev, Viktor; Dzemydaitė, Giedrė: Visualization of data: methods, software, and applications (2019)
  7. Možina, Martin; Demšar, Janez; Bratko, Ivan; Žabkar, Jure: Extreme value correction: a method for correcting optimistic estimations in rule learning (2019)
  8. Viktor Kazakov, Franz J. Király: Machine Learning Automation Toolbox (MLaut) (2019) arXiv
  9. Altarazi, Safwan: Enhancing conformance of injection blow molding by integrating machine learning modeling and Taguchi parameter design (2018)
  10. Gudivada, Venkat N.; Arbabifard, Kamyar: Open-source libraries, application frameworks, and workflow systems for NLP (2018)
  11. Zhang, Chong; Pham, Minh; Fu, Sheng; Liu, Yufeng: Robust multicategory support vector machines using difference convex algorithm (2018)
  12. Perko, Igor: Behaviour-based short-term invoice probability of default evaluation (2017)
  13. Ángel M. García, Francisco Charte, Pedro González, Cristóbal J. Carmona, María J. del Jesus: Subgroup Discovery with Evolutionary Fuzzy Systems in R: The SDEFSR Package (2016) not zbMATH
  14. Bernatavičienė, Jolita; Dzemyda, Gintautas; Kurasova, Olga; Marcinkevičius, Virginijus; Medvedev, Viktor; Treigys, Povilas: Cloud computing approach for intelligent visualization of multidimensional data (2016)
  15. Žabkar, Jure; Bratko, Ivan; Demšar, Janez: Extracting qualitative relations from categorical data (2016)
  16. Coelho, L.P.: Mahotas: Open source software for scriptable computer vision (2013) not zbMATH
  17. Demšar, Janez; Curk, Tomaž; Erjavec, Aleš; Gorup, Črt; Hočevar, Tomaž; Milutinovič, Mitar; Možina, Martin; Polajnar, Matija; Toplak, Marko; Starič, Anže; Štajdohar, Miha; Umek, Lan; Žagar, Lan; Žbontar, Jure; Žitnik, Marinka; Zupan, Blaž: Orange: data mining toolbox in Python (2013)
  18. Miha Štajdohar; Janez Demšar: Interactive Network Exploration with Orange (2013) not zbMATH
  19. Piccolo, Stephen R.; Frey, Lewis J.: ML-flex: a flexible toolbox for performing classification analyses in parallel (2012)