gbm

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 50 articles )

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  1. Berk, Richard A.: Statistical learning from a regression perspective (2020)
  2. Chu, Jianghao; Lee, Tae-Hwy; Ullah, Aman: Component-wise AdaBoost algorithms for high-dimensional binary classification and class probability prediction (2020)
  3. Elman, Miriam R.; Minnier, Jessica; Chang, Xiaohui; Choi, Dongseok: Noise accumulation in high dimensional classification and total signal index (2020)
  4. Mišić, Velibor V.: Optimization of tree ensembles (2020)
  5. Tianhui Zhou, Guangyu Tong, Fan Li, Laine E. Thomas, Fan Li: PSweight: An R Package for Propensity Score Weighting Analysis (2020) arXiv
  6. van den Bergh, Don; Bogaerts, Stefan; Spreen, Marinus; Flohr, Rob; Vandekerckhove, Joachim; Batchelder, William H.; Wagenmakers, Eric-Jan: Cultural consensus theory for the evaluation of patients’ mental health scores in forensic psychiatric hospitals (2020)
  7. 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
  8. Azmi, Mohamed; Runger, George C.; Berrado, Abdelaziz: Interpretable regularized class association rules algorithm for classification in a categorical data space (2019)
  9. Biau, G.; Cadre, B.; Rouvière, L.: Accelerated gradient boosting (2019)
  10. Cerqueira, Vitor; Torgo, Luís; Pinto, Fábio; Soares, Carlos: Arbitrage of forecasting experts (2019)
  11. Choi, Byeong Yeob; Wang, Chen-Pin; Michalek, Joel; Gelfond, Jonathan: Power comparison for propensity score methods (2019)
  12. Ramosaj, Burim; Pauly, Markus: Predicting missing values: a comparative study on non-parametric approaches for imputation (2019)
  13. Tu, Chunhao: Comparison of various machine learning algorithms for estimating generalized propensity score (2019)
  14. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  15. Lee, Simon C. K.; Lin, Sheldon: Delta boosting machine with application to general insurance (2018)
  16. Quan, Zhiyu; Valdez, Emiliano A.: Predictive analytics of insurance claims using multivariate decision trees (2018)
  17. Yukinobu Hamuro; Masakazu Nakamoto; Stephane Cheung; Edward Ip: mbonsai: Application Package for Sequence Classification by Tree Methodology (2018) not zbMATH
  18. Wauters, Mathieu; Vanhoucke, Mario: A nearest neighbour extension to project duration forecasting with artificial intelligence (2017)
  19. Beaudoin, David; Schulte, Oliver; Swartz, Tim B.: Biased penalty calls in the national hockey league (2016)
  20. Blaser, Rico; Fryzlewicz, Piotr: Random rotation ensembles (2016)

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