gbm: Generalized Boosted Regression Models. This package implements extensions to Freund and Schapire’s AdaBoost algorithm and Friedman’s gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

References in zbMATH (referenced in 21 articles )

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  1. Blaser, Rico; Fryzlewicz, Piotr: Random rotation ensembles (2016)
  2. De Bin, Riccardo: Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages \itCoxBoost and \itmboost (2016)
  3. Dubossarsky, E.; Friedman, J.H.; Ormerod, J.T.; Wand, M.P.: Wavelet-based gradient boosting (2016)
  4. Hadiji, Fabian; Molina, Alejandro; Natarajan, Sriraam; Kersting, Kristian: Poisson dependency networks: gradient boosted models for multivariate count data (2015)
  5. Hofner, Benjamin; Mayr, Andreas; Robinzonov, Nikolay; Schmid, Matthias: Model-based boosting in R: a hands-on tutorial using the R package mboost (2014)
  6. Li, Tianxi; Gao, Chao; Xu, Meng; Rajaratnam, Bala: Detecting the impact area of BP deepwater horizon oil discharge: an analysis by time varying coefficient logistic models and boosted trees (2014)
  7. Rao, Marepalli B. (ed.); Rao, C. R. (ed.): Computational statistics with R (2014)
  8. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  9. Hill, Jennifer; Su, Yu-Sung: Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children’s cognitive outcomes (2013)
  10. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  11. Sexton, Joseph; Laake, Petter: Boosted coefficient models (2012)
  12. Shaby, Benjamin A.; Fink, Daniel: Embedding black-box regression techniques into hierarchical Bayesian models (2012)
  13. Karwa, Vishesh; Slavković, Aleksandra B.; Donnell, Eric T.: Causal inference in transportation safety studies: comparison of potential outcomes and causal diagrams (2011)
  14. Williams, Graham: Data Mining with Rattle and R. The art of excavating data for knowledge discovery. (2011)
  15. Chipman, Hugh A.; George, Edward I.; McCulloch, Robert E.: BART: Bayesian additive regression trees (2010)
  16. Kriegler, Brian; Berk, Richard: Small area estimation of the homeless in Los Angeles: an application of cost-sensitive stochastic gradient boosting (2010)
  17. Ward, Gill; Hastie, Trevor; Barry, Simon; Elith, Jane; Leathwick, John R.: Presence-only data and the EM algorithm (2009)
  18. Berk, Richard A.: Statistical learning from a regression perspective (2008)
  19. Sexton, Joseph; Laake, Petter: Logitboost with errors-in-variables (2008)
  20. Bühlmann, Peter; Hothorn, Torsten: Boosting algorithms: regularization, prediction and model fitting (2007)

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