References in zbMATH (referenced in 138 articles )

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  1. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  2. Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
  3. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  4. Farkas, Sébastien; Lopez, Olivier; Thomas, Maud: Cyber claim analysis using generalized Pareto regression trees with applications to insurance (2021)
  5. 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)
  6. Beaulac, Cédric; Rosenthal, Jeffrey S.: BEST: a decision tree algorithm that handles missing values (2020)
  7. 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
  8. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv
  9. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Sparsity in optimal randomized classification trees (2020)
  10. Bommert, Andrea; Sun, Xudong; Bischl, Bernd; Rahnenführer, Jörg; Lang, Michel: Benchmark for filter methods for feature selection in high-dimensional classification data (2020)
  11. Genuer, Robin; Poggi, Jean-Michel: Random forests with R (2020)
  12. Gupta, Bhisham C.; Guttman, Irwin; Jayalath, Kalanka P.: Statistics and probability with applications for engineers and scientists using MINITAB, R and JMP (2020)
  13. Han, Sunwoo; Kim, Hyunjoong; Lee, Yung-Seop: Double random forest (2020)
  14. Madan Gopal Kundu, Samiran Ghosh: Survival trees for right-censored data based on score based parameter instability test (2020) arXiv
  15. 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
  16. Ribeiro, Rita P.; Moniz, Nuno: Imbalanced regression and extreme value prediction (2020)
  17. Sambasivan, Rajiv; Das, Sourish; Sahu, Sujit K.: A Bayesian perspective of statistical machine learning for big data (2020)
  18. Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli: DriveML: An R Package for Driverless Machine Learning (2020) arXiv
  19. Bui, Anh Tuan; Apley, Daniel W.: An exploratory analysis approach for understanding variation in stochastic textured surfaces (2019)
  20. Casalicchio, Giuseppe; Bossek, Jakob; Lang, Michel; Kirchhoff, Dominik; Kerschke, Pascal; Hofner, Benjamin; Seibold, Heidi; Vanschoren, Joaquin; Bischl, Bernd: \textttOpenML: an \textttRpackage to connect to the machine learning platform openml (2019)

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