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Applied Predictive Modeling

Citations

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Cited by:

  1. R. L. Manogna & Ashray Kashyap & Samyak Sanat Jain, 2025. "Is there a universal fit? Employing machine learning to investigate the diversity and prominence of factors influencing early-stage entrepreneurship," Journal of Innovation and Entrepreneurship, Springer, vol. 14(1), pages 1-26, December.
  2. Paul Praveen Kumar Ashok, 2020. "Advanced Data Modeling Techniques in Power BI for Enterprise Analytics," International Journal of Computing and Engineering, CARI Journals Limited, vol. 1(2), pages 32-42.
  3. Renato P. Colistete, 2021. "Predicting Skills of Runaway Slaves in Sao Paulo, 1854-1887," Working Papers, Department of Economics 2021_15, University of São Paulo (FEA-USP), revised 23 Apr 2021.
  4. Rafael Quintana, 2023. "The Structure of Academic Achievement: Searching for Proximal Mechanisms Using Causal Discovery Algorithms," Sociological Methods & Research, , vol. 52(1), pages 85-134, February.
  5. Almudena Moreno-Ribera & Aida Calviño, 2025. "Double-weighted kNN: a simple and efficient variant with embedded feature selection," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(4), pages 989-999, December.
  6. Rahul Kumar & Rahul Thakurta, 2025. "Classifying DSS Research – A Theoretical Framework," Information Systems Frontiers, Springer, vol. 27(5), pages 1759-1788, October.
  7. Sha, Xiaowen & Su, Miao, 2026. "Ownership, regulation, and ESG in transport and logistics: Insights for policy from explainable machine learning," Transport Policy, Elsevier, vol. 178(C).
  8. Deslatte, Aaron & Scott, Tyler A. & Carter, David P., 2019. "Specialized governance and regional land-use outcomes: A spatial analysis of Florida community development districts," Land Use Policy, Elsevier, vol. 83(C), pages 227-239.
  9. Dossa, Joel Victor & Ukwuoma, Chiagoziem C. & Thomas, Dara & Dossa, James Mhoja & Gopang, Aamir Ali, 2025. "Prediction of nexus among ESG disclosure and firm Performance: Applicability, explainability and implications," Innovation and Green Development, Elsevier, vol. 4(4).
  10. Sidique Gawusu & Seidu Abdulai Jamatutu & Xiaobing Zhang & Solahudeen Tando Moomin & Abubakari Ahmed & Rhoda Afriyie Mensah & Oisik Das & Ishmael Ackah, 2026. "Spatial analysis and predictive modeling of energy poverty: insights for policy implementation," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 28(1), pages 851-898, January.
  11. Huynh, Tran & Uebelmesser, Silke, 2024. "Early warning models for systemic banking crises: Can political indicators improve prediction?," European Journal of Political Economy, Elsevier, vol. 81(C).
  12. Marina Romeo & Montserrat Yepes-Baldó & Josep Pascual, 2025. "Understanding Gendered Employment Dynamics: Demographic Profiles and Characteristics of Individuals With Disabilities in Spain," SAGE Open, , vol. 15(4), pages 21582440251, November.
  13. Adamecz-Völgyi, Anna & Henderson, Morag & Shure, Nikki, 2020. "Is ‘first in family’ a good indicator for widening university participation?," Economics of Education Review, Elsevier, vol. 78(C).
  14. Daniel Horn & Tobias Markus Krabel & Thi Ngoc Tien Tran & Andreas Groll & Carsten Jentsch, 2026. "The impact of random tree depth—a novel randomization process for ensemble methods," Computational Statistics, Springer, vol. 41(1), pages 1-28, January.
  15. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
  16. Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
  17. Abdullah S. Al-Jawarneh & Ahmed R. M. Alsayed & Heba N. Ayyoub & Mohd Tahir Ismail & Siok Kun Sek & Kivanç Halil Ariç & Giancarlo Manzi, 2024. "Enhancing Model Selection by Obtaining Optimal Tuning Parameters in Elastic-Net Quantile Regression, Application to Crude Oil Prices," JRFM, MDPI, vol. 17(8), pages 1-19, July.
  18. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
  19. Taras, Vas & Rickley, Marketa & Alon, Ilan & Dong, Longzhu & Malmin, Hilde, 2025. "Predictors of Cultural Intelligence: Automated Machine Learning vs. PLS-SEM," Journal of International Management, Elsevier, vol. 31(5).
  20. Tomasz Pisula, 2020. "An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship," JRFM, MDPI, vol. 13(2), pages 1-35, February.
  21. Kimura, Takuma, 2025. "Exploring the Frontier: Generative AI Applications in Online Consumer Behavior Analytics," Cuadernos de Gestión, Universidad del País Vasco - Instituto de Economía Aplicada a la Empresa (IEAE).
  22. Ghurumuruhan Ganesan, 2026. "Maximum Weight of Stable Sets in Non-Sparse and Inhomogeneous Random Graphs," Journal of Theoretical Probability, Springer, vol. 39(1), pages 1-31, March.
  23. Kawrani, Sara & Abi Almona, Ossama & Ibrahim, Mira & Ibrahim, Moustafa & Obeid, Emil, 2026. "A comprehensive review of green hydrogen production via electrolysis and thermolysis, and the prediction of potential natural hydrogen (aka gold hydrogen) presence using machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 230(C).
  24. Rahul Kumar & Shubhadeep Mukherjee & Divya Choudhary, 2025. "Uncovering the roots of customer dissatisfaction via Amazon reviews: a hybrid ensemble-deep learning approach for E-commerce quality management," Annals of Operations Research, Springer, vol. 353(2), pages 545-574, October.
  25. repec:bcp:journl:v:9:y:2025:i:12:p:2590-2605 is not listed on IDEAS
  26. Sibylle Gerlach & Xiaohua Yu & Jan-Henning Feil, 2026. "Success factors of agrifood start-ups – perspectives from machine learning," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 14(1), pages 1-28, December.
  27. 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.
  28. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
  29. Barone, Guglielmo & Letta, Marco, 2025. "Unlevel playing field? Machine learning meets state aid regulation," International Journal of Industrial Organization, Elsevier, vol. 101(C).
  30. Ghurumuruhan Ganesan, 2026. "Reduced Similarity Decompositions and Subsets of Random Categorical Datasets," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 88(1), pages 136-159, February.
  31. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
  32. Aditi Nautiyal & Amit Kumar Mishra, 2025. "Machine learning approach for intelligent prediction of petroleum upstream stuck pipe challenge in oil and gas industry," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(10), pages 24167-24193, October.
  33. Peiró-Signes, Ángel & Labarcés-Ballestas, Carlos & Segarra-Oña, Marival & Trull-Domínguez, Óscar, 2026. "Leave me be, make me strong! Growing solo to innovate together. Results from machine learning techniques on the innovation performance in resource-constrained manufacturing industries," Socio-Economic Planning Sciences, Elsevier, vol. 103(C).
  34. Gavasso-Rita, Yohanne Larissa & Zaerpour, Masoud & Abdelmoaty, Hebatallah & Li, Yanping & Elshorbagy, Amin & Schuster-Wallace, Corinne & Paschalis, Athanasios & Papalexiou, Simon Michael, 2025. "Rainfed spring canola yield response to changing heat and water stress in the Canadian Prairie region," Agricultural Water Management, Elsevier, vol. 322(C).
  35. Pinto, Claudio, 2025. "Combining machine learning techniques with NDEA methodology: the use of R.F. and A.N.N," MPRA Paper 126539, University Library of Munich, Germany.
  36. Mao, Yaqi & Yu, Xiaobing & Wang, Feng & Zhu, Junhua, 2026. "Electric vehicle charging demand forecasting: A data-driven integrated learning approach," Renewable Energy, Elsevier, vol. 256(PD).
  37. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
  38. Lei Xu & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform," JRFM, MDPI, vol. 11(4), pages 1-11, December.
  39. Md. Salman & Mou Rani Sarker & Md. Asifur Rahman & Andrew M. McKenzie & Md Abdur Rouf Sarkar, 2026. "Breaking the Regional Barriers: Identifying Determinants of Antenatal Care Access in Bangladesh for Improved Maternal Health Policy," Sustainable Development, John Wiley & Sons, Ltd., vol. 34(2), pages 2925-2962, April.
  40. Maksim Malyy & Zeljko Tekic & Tatiana Podladchikova, 2026. "Hey Google, how valuable is that startup? Internet search queries and new ventures’ valuation—insights from B2B and B2C sectors," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-26, December.
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