ada

ada: An R Package for Stochastic Boosting. Boosting is an iterative algorithm that combines simple classification rules with ”mediocre” performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.


References in zbMATH (referenced in 17 articles , 1 standard article )

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  1. Chu, Jianghao; Lee, Tae-Hwy; Ullah, Aman: Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability prediction (2020)
  2. Vinod, Hrishikesh D. (ed.); Rao, C. R. (ed.): Financial, macro and micro econometrics using R (2020)
  3. Arioli, Gianni; Koch, Hans: Spectral stability for the wave equation with periodic forcing (2018)
  4. Bogaert, Matthias; Ballings, Michel; Van den Poel, Dirk: Evaluating the importance of different communication types in romantic tie prediction on social media (2018)
  5. Bogaert, Matthias; Ballings, Michel; Hosten, Martijn; Van den Poel, Dirk: Identifying soccer players on Facebook through predictive analytics (2017)
  6. Conversano, Claudio; Dusseldorp, Elise: Modeling threshold interaction effects through the logistic classification trunk (2017)
  7. Kotthaus, Helena; Korb, Ingo; Lang, Michel; Bischl, Bernd; Rahnenführer, Jörg; Marwedel, Peter: Runtime and memory consumption analyses for machine learning R programs (2015)
  8. Kang, Chaeryon; Janes, Holly; Huang, Ying: Combining biomarkers to optimize patient treatment recommendations (2014)
  9. Esteban Alfaro; Matias Gamez; Noelia García: adabag: An R Package for Classification with Boosting and Bagging (2013) not zbMATH
  10. Larese, Mónica G.; Granitto, Pablo M.; Gómez, Juan C.: Spot defects detection in cDNA microarray images (2013) ioport
  11. Williams, Graham: Data Mining with Rattle and R. The art of excavating data for knowledge discovery. (2011)
  12. Andreas Borg, Murat Sariyar: The RecordLinkage Package: Detecting Errors in Data (2010) not zbMATH
  13. Kriegler, Brian; Berk, Richard: Small area estimation of the homeless in Los Angeles: an application of cost-sensitive stochastic gradient boosting (2010)
  14. Adler, Werner; Lausen, Berthold: Bootstrap estimated true and false positive rates and ROC curve (2009)
  15. Max Kuhn: Building Predictive Models in R Using the caret Package (2008) not zbMATH
  16. Mark Culp; Kjell Johnson; George Michailides: ada: An R Package for Stochastic Boosting (2006) not zbMATH
  17. Solaeche Galera, María Cristina: Lady Ada Byron and the first program for computers (1994)