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 31 articles )

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  1. Alireza S. Mahani; Mansour T.A. Sharabiani: Bayesian, and Non-Bayesian, Cause-Specific Competing-Risk Analysis for Parametric and Nonparametric Survival Functions: The R Package CFC (2019) not zbMATH
  2. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  3. Lee, Simon C. K.; Lin, Sheldon: Delta boosting machine with application to general insurance (2018)
  4. Yukinobu Hamuro; Masakazu Nakamoto; Stephane Cheung; Edward Ip: mbonsai: Application Package for Sequence Classification by Tree Methodology (2018) not zbMATH
  5. Wauters, Mathieu; Vanhoucke, Mario: A nearest neighbour extension to project duration forecasting with artificial intelligence (2017)
  6. Blaser, Rico; Fryzlewicz, Piotr: Random rotation ensembles (2016)
  7. De Bin, Riccardo: Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages \textitCoxBoostand \textitmboost (2016)
  8. Dubossarsky, E.; Friedman, J. H.; Ormerod, J. T.; Wand, M. P.: Wavelet-based gradient boosting (2016)
  9. Hadiji, Fabian; Molina, Alejandro; Natarajan, Sriraam; Kersting, Kristian: Poisson dependency networks: gradient boosted models for multivariate count data (2015)
  10. Goldsmith, Jeff; Scheipl, Fabian: Estimator selection and combination in scalar-on-function regression (2014)
  11. Hofner, Benjamin; Mayr, Andreas; Robinzonov, Nikolay; Schmid, Matthias: Model-based boosting in R: a hands-on tutorial using the R package mboost (2014)
  12. 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)
  13. Rao, Marepalli B. (ed.); Rao, C. R. (ed.): Computational statistics with R (2014)
  14. Weihs, Claus; Mersmann, Olaf; Ligges, Uwe: Foundations of statistical algorithms. With references to R packages (2014)
  15. Esteban Alfaro; Matias Gamez; Noelia García: adabag: An R Package for Classification with Boosting and Bagging (2013) not zbMATH
  16. 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)
  17. Kuhn, Max; Johnson, Kjell: Applied predictive modeling (2013)
  18. Sexton, Joseph; Laake, Petter: Boosted coefficient models (2012)
  19. Shaby, Benjamin A.; Fink, Daniel: Embedding black-box regression techniques into hierarchical Bayesian models (2012)
  20. Karwa, Vishesh; Slavković, Aleksandra B.; Donnell, Eric T.: Causal inference in transportation safety studies: comparison of potential outcomes and causal diagrams (2011)

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