References in zbMATH (referenced in 154 articles )

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  1. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: On sparse optimal regression trees (2022)
  2. Gu, Yu; Preisser, John S.; Zeng, Donglin; Shrestha, Poojan; Shah, Molina; Simancas-Pallares, Miguel A.; Ginnis, Jeannie; Divaris, Kimon: Partitioning around medoids clustering and random forest classification for GIS-informed imputation of fluoride concentration data (2022)
  3. Johnson, Michael; Cao, Jiongyi; Kang, Hyunseung: Detecting heterogeneous treatment effects with instrumental variables and application to the Oregon Health Insurance Experiment (2022)
  4. Kim, Ahhyoun; Kim, Hyunjoong: A new classification tree method with interaction detection capability (2022)
  5. Nestler, Steffen; Humberg, Sarah: A Lasso and a regression tree mixed-effect model with random effects for the level, the residual variance, and the autocorrelation (2022)
  6. Wang, Hengjie; Planas, Robert; Chandramowlishwaran, Aparna; Bostanabad, Ramin: Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (2022)
  7. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  8. Blanquero, Rafael; Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Optimal randomized classification trees (2021)
  9. Carrizosa, Emilio; Molero-Río, Cristina; Romero Morales, Dolores: Mathematical optimization in classification and regression trees (2021)
  10. Conde, David; Fernández, Miguel A.; Rueda, Cristina; Salvador, Bonifacio: Isotonic boosting classification rules (2021)
  11. da Silva, Natalia; Cook, Dianne; Lee, Eun-Kyung: A projection pursuit forest algorithm for supervised classification (2021)
  12. Farkas, Sébastien; Lopez, Olivier; Thomas, Maud: Cyber claim analysis using generalized Pareto regression trees with applications to insurance (2021)
  13. Günlük, Oktay; Kalagnanam, Jayant; Li, Minhan; Menickelly, Matt; Scheinberg, Katya: Optimal decision trees for categorical data via integer programming (2021)
  14. 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)
  15. Krzysztof Gajowniczek, Tomasz Ząbkowski: ImbTreeEntropy: An R package for building entropy-based classification trees on imbalanced datasets (2021) not zbMATH
  16. 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
  17. Oune, Nicholas; Bostanabad, Ramin: Latent map Gaussian processes for mixed variable metamodeling (2021)
  18. Beaulac, Cédric; Rosenthal, Jeffrey S.: BEST: a decision tree algorithm that handles missing values (2020)
  19. 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
  20. Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani: Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties (2020) arXiv

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