rpart

R package rpart: Recursive Partitioning. Recursive partitioning and regression trees. Recursive partitioning for classification, regression and survival trees. An implementation of most of the functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.

Keywords for this software

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References in zbMATH (referenced in 142 articles )

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  1. Kim, Ahhyoun; Kim, Hyunjoong: A new classification tree method with interaction detection capability (2022)
  2. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  3. Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
  4. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  5. Farkas, Sébastien; Lopez, Olivier; Thomas, Maud: Cyber claim analysis using generalized Pareto regression trees with applications to insurance (2021)
  6. Günlük, Oktay; Kalagnanam, Jayant; Li, Minhan; Menickelly, Matt; Scheinberg, Katya: Optimal decision trees for categorical data via integer programming (2021)
  7. Javadi, Sara; Bahrampour, Abbas; Saber, Mohammad Mehdi; Garrusi, Behshid; Baneshi, Mohammad Reza: Evaluation of four multiple imputation methods for handling missing binary outcome data in the presence of an interaction between a dummy and a continuous variable (2021)
  8. Krzysztof Gajowniczek, Tomasz Ząbkowski: ImbTreeEntropy: An R package for building entropy-based classification trees on imbalanced datasets (2021) not zbMATH
  9. 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
  10. Beaulac, Cédric; Rosenthal, Jeffrey S.: BEST: a decision tree algorithm that handles missing values (2020)
  11. Begüm D. Topçuoğlu; Zena Lapp; Kelly L. Sovacool; Evan Snitkin; Jenna Wiens; Patrick D. Schloss: mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines (2020) not zbMATH
  12. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
  13. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  14. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  15. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  16. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  17. Han, Sunwoo; Kim, Hyunjoong; Lee, Yung-Seop: Double random forest (2020)
  18. Madan Gopal Kundu, Samiran Ghosh: Survival trees for right-censored data based on score based parameter instability test (2020) arXiv
  19. Nicolas R. Lauve, Stuart J. Nelson, S. Stanley Young, Robert L. Obenchain, Christophe G. Lambert: LocalControl: An R Package for Comparative Safety and Effectiveness Research (2020) not zbMATH
  20. Ribeiro, Rita P.; Moniz, Nuno: Imbalanced regression and extreme value prediction (2020)

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