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Applied Predictive Modeling

Author

Listed:
  • Max Kuhn

    (Pfizer Global Research and Development, Division of Nonclinical Statistics)

  • Kjell Johnson

    (Arbor Analytics)

Abstract

No abstract is available for this item.

Individual chapters are listed in the "Chapters" tab

Suggested Citation

  • Max Kuhn & Kjell Johnson, 2013. "Applied Predictive Modeling," Springer Books, Springer, number 978-1-4614-6849-3, January.
    • Handle: RePEc:spr:sprbok:978-1-4614-6849-3
    DOI: 10.1007/978-1-4614-6849-3
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. 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).
    7. 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.
    8. Mohamed Hanafy & Ruixing Ming, 2021. "Machine Learning Approaches for Auto Insurance Big Data," Risks, MDPI, vol. 9(2), pages 1-23, February.
    9. 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.
    10. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    11. 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.
    12. 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.
    13. 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.
    14. Barone, Guglielmo & Letta, Marco, 2025. "Unlevel playing field? Machine learning meets state aid regulation," International Journal of Industrial Organization, Elsevier, vol. 101(C).
    15. 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).
    16. 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.
    17. 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.
    18. 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.

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