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evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R

Citations

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Cited by:

  1. Susan Athey & Stefan Wager, 2021. "Policy Learning With Observational Data," Econometrica, Econometric Society, vol. 89(1), pages 133-161, January.
  2. Yves Staudt & Joël Wagner, 2021. "Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance," Risks, MDPI, vol. 9(3), pages 1-28, March.
  3. Max Tabord-Meehan, 2023. "Stratification Trees for Adaptive Randomisation in Randomised Controlled Trials," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2646-2673.
  4. Vrigazova Borislava, 2021. "The Proportion for Splitting Data into Training and Test Set for the Bootstrap in Classification Problems," Business Systems Research, Sciendo, vol. 12(1), pages 228-242, May.
  5. Islam, Towhidul & Meade, Nigel & Carson, Richard T. & Louviere, Jordan J. & Wang, Juan, 2022. "The usefulness of socio-demographic variables in predicting purchase decisions: Evidence from machine learning procedures," Journal of Business Research, Elsevier, vol. 151(C), pages 324-338.
  6. Meryem Pulat & İpek Deveci Kocakoç, 2024. "Classification with machine learning algorithms after hybrid feature selection in imbalanced data sets," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(4), pages 157-183.
  7. Emmanuel Jordy Menvouta & Jolien Ponnet & Robin Van Oirbeek & Tim Verdonck, 2022. "mCube: Multinomial Micro-level reserving Model," Papers 2212.00101, arXiv.org.
  8. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
  9. Alvarez-Iglesias, Alberto & Hinde, John & Ferguson, John & Newell, John, 2017. "An alternative pruning based approach to unbiased recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 90-102.
  10. Ronilo Ragodos & Tong Wang, 2022. "Disjunctive Rule Lists," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3259-3276, November.
  11. Giulia Vannucci & Anna Gottard, 2023. "An evolutionary estimation procedure for generalized semilinear regression trees," Computational Statistics, Springer, vol. 38(4), pages 1927-1946, December.
  12. Claudio Conversano & Elise Dusseldorp, 2017. "Modeling Threshold Interaction Effects Through the Logistic Classification Trunk," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 399-426, October.
  13. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
  14. Patrick Rehill, 2024. "Distilling interpretable causal trees from causal forests," Papers 2408.01023, arXiv.org.
  15. Fernandez Martinez, Roberto & Lostado Lorza, Ruben & Santos Delgado, Ana Alexandra & Piedra, Nelson, 2021. "Use of classification trees and rule-based models to optimize the funding assignment to research projects: A case study of UTPL," Journal of Informetrics, Elsevier, vol. 15(1).
  16. Patrick Rehill & Nicholas Biddle, 2025. "Policy Learning for Many Outcomes of Interest: Combining Optimal Policy Trees with Multi-objective Bayesian Optimisation," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 971-1001, August.
  17. Patrick Rehill & Nicholas Biddle, 2022. "Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation," Papers 2212.06312, arXiv.org, revised Oct 2023.
  18. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
  19. Höppner, Sebastiaan & Stripling, Eugen & Baesens, Bart & Broucke, Seppe vanden & Verdonck, Tim, 2020. "Profit driven decision trees for churn prediction," European Journal of Operational Research, Elsevier, vol. 284(3), pages 920-933.
  20. Roberto Chiosa & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings," Energies, MDPI, vol. 14(1), pages 1-28, January.
  21. Anja Breuer & Yves Staudt, 2022. "Equalization Reserves for Reinsurance and Non-Life Undertakings in Switzerland," Risks, MDPI, vol. 10(3), pages 1-41, March.
  22. Lucas Reck & Johannes Schupp & Andreas Reuß, 2025. "A Multistate Analysis of Policyholder Behaviour in Life Insurance—Lasso-Based Modelling Approaches," Risks, MDPI, vol. 13(4), pages 1-28, April.
  23. Davide Natalini & Giangiacomo Bravo & Aled Wynne Jones, 2019. "Global food security and food riots – an agent-based modelling approach," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(5), pages 1153-1173, October.
  24. Chi-Chang Chang & Tse-Hung Huang & Pei-Wei Shueng & Ssu-Han Chen & Chun-Chia Chen & Chi-Jie Lu & Yi-Ju Tseng, 2021. "Developing a Stacked Ensemble-Based Classification Scheme to Predict Second Primary Cancers in Head and Neck Cancer Survivors," IJERPH, MDPI, vol. 18(23), pages 1-10, November.
  25. Selcuk Bayraci, 2017. "Application of profit-based credit scoring models using R," Romanian Statistical Review, Romanian Statistical Review, vol. 65(4), pages 3-28, December.
  26. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
  27. Hajko, Vladimír, 2017. "The failure of Energy-Economy Nexus: A meta-analysis of 104 studies," Energy, Elsevier, vol. 125(C), pages 771-787.
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