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The random forest algorithm for statistical learning

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Cited by:

  1. Sascha O. Becker, Sascha O & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," The Warwick Economics Research Paper Series (TWERPS) 1478, University of Warwick, Department of Economics.
  2. Becker, Sascha O. & Voth, Hans-Joachim, 2023. "From the Death of God to the Rise of Hitler," CAGE Online Working Paper Series 688, Competitive Advantage in the Global Economy (CAGE).
  3. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
  4. Hillebrecht, Michael & Klonner, Stefan & Pacere, Noraogo A., 2020. "Dynamic Properties of Poverty Targeting," Working Papers 0696, University of Heidelberg, Department of Economics.
  5. Junlong Zhang & Youbin He & Yuan Zhang & Weifeng Li & Junjie Zhang, 2022. "Well-Logging-Based Lithology Classification Using Machine Learning Methods for High-Quality Reservoir Identification: A Case Study of Baikouquan Formation in Mahu Area of Junggar Basin, NW China," Energies, MDPI, vol. 15(10), pages 1-15, May.
  6. Forbes, Kevin F., 2023. "Demand for grid-supplied electricity in the presence of distributed solar energy resources: Evidence from New York City," Utilities Policy, Elsevier, vol. 80(C).
  7. Young Jae Kim, 2021. "Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography," IJERPH, MDPI, vol. 18(16), pages 1-14, August.
  8. Ivan Brandić & Alan Antonović & Lato Pezo & Božidar Matin & Tajana Krička & Vanja Jurišić & Karlo Špelić & Mislav Kontek & Juraj Kukuruzović & Mateja Grubor & Ana Matin, 2023. "Energy Potentials of Agricultural Biomass and the Possibility of Modelling Using RFR and SVM Models," Energies, MDPI, vol. 16(2), pages 1-10, January.
  9. Kang, Lili & Zhao, Guangchuan, 2022. "Financial support for unmet need for personal assistance with daily activities: Implications from China's long-term care insurance pilots," Finance Research Letters, Elsevier, vol. 45(C).
  10. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer, 2023. "pystacked: Stacking generalization and machine learning in Stata," Stata Journal, StataCorp LP, vol. 23(4), pages 909-931, December.
  11. Virginia Negri & Alessandro Mingotti & Roberto Tinarelli & Lorenzo Peretto, 2023. "Comparison of Algorithms for the AI-Based Fault Diagnostic of Cable Joints in MV Networks," Energies, MDPI, vol. 16(1), pages 1-20, January.
  12. Özer Depren & Mustafa Tevfik Kartal & Serpil Kılıç Depren, 2021. "Recent innovation in benchmark rates (BMR): evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-20, December.
  13. Xue, Shaobo & Ma, Bo & Wang, Chenguang & Li, Zhanbin, 2023. "Identifying key landscape pattern indices influencing the NPP: A case study of the upper and middle reaches of the Yellow River," Ecological Modelling, Elsevier, vol. 484(C).
  14. Julien Champagne & Émilien Gouin-Bonenfant, 2022. "Monetary Policy, Credit Constraints and SME Employment," Staff Working Papers 22-49, Bank of Canada.
  15. Tomasz Rymarczyk & Konrad Niderla & Edward Kozłowski & Krzysztof Król & Joanna Maria Wyrwisz & Sylwia Skrzypek-Ahmed & Piotr Gołąbek, 2021. "Logistic Regression with Wave Preprocessing to Solve Inverse Problem in Industrial Tomography for Technological Process Control," Energies, MDPI, vol. 14(23), pages 1-21, December.
  16. Wang, Feipeng & Wong, Wing-Keung & Wang, Zheng & Albasher, Gadah & Alsultan, Nouf & Fatemah, Ambreen, 2023. "Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions," Resources Policy, Elsevier, vol. 85(PA).
  17. Merike Kukk & Jaanika Meriküll & Tairi Rõõm, 2023. "The Gender Wealth Gap in Europe: Application of Machine Learning to Predict Individual‐level Wealth," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(2), pages 289-317, June.
  18. Zhennan Wu, 2022. "Using Machine Learning Approach to Evaluate the Excessive Financialization Risks of Trading Enterprises," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1607-1625, April.
  19. İbrahim Özmen & Şerife Özşahin, 2023. "Effects of global energy and price fluctuations on Turkey's inflation: new evidence," Economic Change and Restructuring, Springer, vol. 56(4), pages 2695-2728, August.
  20. Jia-Qi, Liu & Yun-Wen, Feng & Da, Teng & Jun-Yu, Chen & Cheng, Lu, 2023. "Operational reliability evaluation and analysis framework of civil aircraft complex system based on intelligent extremum machine learning model," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
  21. Tymoteusz Miller & Grzegorz Mikiciuk & Anna Kisiel & Małgorzata Mikiciuk & Dominika Paliwoda & Lidia Sas-Paszt & Danuta Cembrowska-Lech & Adrianna Krzemińska & Agnieszka Kozioł & Adam Brysiewicz, 2023. "Machine Learning Approaches for Forecasting the Best Microbial Strains to Alleviate Drought Impact in Agriculture," Agriculture, MDPI, vol. 13(8), pages 1-16, August.
  22. Jialing Zhang & Zhanxu Chen & An Wang & Zhenzhang Li & Wei Wan, 2023. "Intelligent Personalized Lighting Control System for Residents," Sustainability, MDPI, vol. 15(21), pages 1-12, October.
  23. Lee, Seungmin & Barrett, Christopher B. & Hoddinott, John F., 2021. "Food Security Dynamics in the United States, 2001-2017," Working Papers 316604, Cornell University, Department of Applied Economics and Management.
  24. Adam Kula & Albert Smalcerz & Maciej Sajkowski & Zygmunt Kamiński, 2021. "Analysis of Office Rooms Energy Consumption Data in Respect to Meteorological and Direct Sun Exposure Conditions," Energies, MDPI, vol. 14(22), pages 1-20, November.
  25. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
  26. Wang, Sicheng & Noland, Robert B., 2021. "What is the elasticity of sharing a ridesourcing trip?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 284-305.
  27. Almudena Sanjurjo-de-No & Ana María Pérez-Zuriaga & Alfredo García, 2023. "Factors Influencing the Pedestrian Injury Severity of Micromobility Crashes," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
  28. Uttam Khatri & Ji-In Kim & Goo-Rak Kwon, 2023. "Genetics Information with Functional Brain Networks for Dementia Classification," Mathematics, MDPI, vol. 11(6), pages 1-20, March.
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