Open-source machine learning: R meets Weka. The Waikato Environment for Knowleage Analysis (Weka) is an open-source project in machine learning covering classification, regression, clustering, association rules and visualization. It is implemented on Java and released under GPL. This paper is devoted to the Weka interface for R-software provided by the R extension package RWeka. The interfacing methodology, limitations and possible extensions are discussed.

References in zbMATH (referenced in 21 articles , 1 standard article )

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  1. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  2. Krzysztof Gajowniczek, Tomasz Ząbkowski: ImbTreeEntropy: An R package for building entropy-based classification trees on imbalanced datasets (2021) not zbMATH
  3. Krzysztof Gajowniczek; Tomasz Ząbkowski: ImbTreeAUC: An R package for building classification trees using the area under the ROC curve (AUC) on imbalanced datasets (2021) not zbMATH
  4. Wang, Tong; Lin, Qihang: Hybrid predictive models: when an interpretable model collaborates with a black-box model (2021)
  5. F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J. M. Benítez: FSinR: an exhaustive package for feature selection (2020) arXiv
  6. Ferri, Cèsar; Hernández-Orallo, José; Flach, Peter: Setting decision thresholds when operating conditions are uncertain (2019)
  7. Gilles Kratzer, Fraser Iain Lewis, Arianna Comin, Marta Pittavino, Reinhard Furrer: Additive Bayesian Network Modelling with the R Package abn (2019) arXiv
  8. Philipp, Michel; Rusch, Thomas; Hornik, Kurt; Strobl, Carolin: Measuring the stability of results from supervised statistical learning (2018)
  9. Reto Bürgin; Gilbert Ritschard: Coefficient-Wise Tree-Based Varying Coefficient Regression with vcrpart (2017) not zbMATH
  10. Azam, Muhammad; Aslam, Muhammad; Pfeiffer, Karl Peter: Three steps strategy to search for optimum classification trees (2016)
  11. Hernández-Orallo, José; Ferri, Cèsar; Lachiche, Nicolas; Martínez-Usó, Adolfo; Ramírez-Quintana, M. José: Binarised regression tasks: methods and evaluation metrics (2016)
  12. Hothorn, Torsten: partykit: a modular toolkit for recursive partytioning in \textttR (2015)
  13. Henelius, Andreas; Puolamäki, Kai; Boström, Henrik; Asker, Lars; Papapetrou, Panagiotis: A peek into the black box: exploring classifiers by randomization (2014) ioport
  14. Thomas Grubinger; Achim Zeileis; Karl-Peter Pfeiffer: evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R (2014) not zbMATH
  15. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  16. Mehmood, Rashid; Riaz, Muhammad; Does, Ronald J. M. M.: Efficient power computation for (r) out of (m) runs rules schemes (2013)
  17. Rusch, Thomas; Zeileis, Achim: Gaining insight with recursive partitioning of generalized linear models (2013)
  18. Brogini, Adriana; Slanzi, Debora: On using Bayesian networks for complexity reduction in decision trees (2010)
  19. Hornik, Kurt; Buchta, Christian; Zeileis, Achim: Open-source machine learning: R meets Weka (2009)
  20. Urbanek, Simon: How to talk to strangers: ways to leverage connectivity between R, Java and Objective C (2009)

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