IDEAS home Printed from https://ideas.repec.org/a/aes/infoec/v25y2021i2p64-74.html
   My bibliography  Save this article

Basic Hyperparameters Tuning Methods for Classification Algorithms

Author

Listed:
  • Claudia ANTAL-VAIDA

Abstract

Considering the dynamics of the economic environment and the amount of data generated every second, the decision-making process is changing and becomes data driven, highly influencing the business strategies setup in order to keep the competitive advantage. However, without technology, data analysis would not be feasible, reason why machine learning is seen as a disruptive innovation for businesses, especially due to its capacity to convert data into actionable outcomes. Though, for a high-quality machine learning model result, algorithm selection and hyperparameters optimization play vital roles, hence became high-interest topics in the field. To achieve this, various automatic selection methods have been proposed and the aim of this paper is to compare two of them – GridSearch and RandomizedSearch - and assess their impact on the model accuracy by comparing with the results obtained when default hyperparameters were applied.

Suggested Citation

  • Claudia ANTAL-VAIDA, 2021. "Basic Hyperparameters Tuning Methods for Classification Algorithms," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 25(2), pages 64-74.
    • Handle: RePEc:aes:infoec:v:25:y:2021:i:2:p:64-74
    as

    Download full text from publisher

    File URL: http://revistaie.ase.ro/content/98/06%20-%20antal.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jing Wang & Weisheng Lu & Fan Xue & Meng Ye, 2021. "A Machine Learning-Based Approach for BIM Object Localization," Springer Books, in: Gui Ye & Hongping Yuan & Jian Zuo (ed.), Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, pages 1391-1399, Springer.
    2. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
    3. Raymond C. W. Leung & Yu-Man Tam, 2021. "Statistical Arbitrage Risk Premium by Machine Learning," Papers 2103.09987, arXiv.org.
    4. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
    5. Zhang, Liang & Wen, Jin & Li, Yanfei & Chen, Jianli & Ye, Yunyang & Fu, Yangyang & Livingood, William, 2021. "A review of machine learning in building load prediction," Applied Energy, Elsevier, vol. 285(C).
    6. Christian M. Dahl & Torben S. D. Johansen & Emil N. S{o}rensen & Christian E. Westermann & Simon F. Wittrock, 2021. "Applications of Machine Learning in Document Digitisation," Papers 2102.03239, arXiv.org.
    7. Lee, In & Lee, Kyoochun, 2015. "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, Elsevier, vol. 58(4), pages 431-440.
    8. Hao Ni & Xin Dong & Jinsong Zheng & Guangxi Yu, 2021. "An Introduction to Machine Learning in Quantitative Finance," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number q0275, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caratozzolo, Vincenzo & Misuri, Alessio & Cozzani, Valerio, 2022. "A generalized equipment vulnerability model for the quantitative risk assessment of horizontal vessels involved in Natech scenarios triggered by floods," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    2. Benlagha, Noureddine & Hemrit, Wael, 2020. "Internet use and insurance growth: evidence from a panel of OECD countries," Technology in Society, Elsevier, vol. 62(C).
    3. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
    4. Charalampopoulos, George & Katsianis, Dimitris & Varoutas, Dimitris, 2020. "Investigating the intertwining impact of wholesale access pricing and the commitment to net neutrality principle on European next-generation access networks private investment plans: An options-game a," Telecommunications Policy, Elsevier, vol. 44(3).
    5. Elsisi, Mahmoud & Amer, Mohammed & Dababat, Alya’ & Su, Chun-Lien, 2023. "A comprehensive review of machine learning and IoT solutions for demand side energy management, conservation, and resilient operation," Energy, Elsevier, vol. 281(C).
    6. Leonel Jorge Ribeiro Nunes & Radu Godina & João Carlos de Oliveira Matias, 2019. "Technological Innovation in Biomass Energy for the Sustainable Growth of Textile Industry," Sustainability, MDPI, vol. 11(2), pages 1-12, January.
    7. Nino Paresashvili & Maia Nikvashvili, 2019. "Career Management Peculiarities in Educational Institutions," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 5, January -.
    8. Manuel Jaramillo & Wilson Pavón & Lisbeth Jaramillo, 2024. "Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review," Data, MDPI, vol. 9(1), pages 1-23, January.
    9. Gao, Datong & Zhao, Bin & Kwan, Trevor Hocksun & Hao, Yong & Pei, Gang, 2022. "The spatial and temporal mismatch phenomenon in solar space heating applications: status and solutions," Applied Energy, Elsevier, vol. 321(C).
    10. Athanasios Tsipis & Asterios Papamichail & Ioannis Angelis & George Koufoudakis & Georgios Tsoumanis & Konstantinos Oikonomou, 2020. "An Alertness-Adjustable Cloud/Fog IoT Solution for Timely Environmental Monitoring Based on Wildfire Risk Forecasting," Energies, MDPI, vol. 13(14), pages 1-35, July.
    11. Dahl, Christian M. & Johansen, Torben S.D. & Sørensen, Emil N. & Wittrock, Simon, 2023. "HANA: A handwritten name database for offline handwritten text recognition," Explorations in Economic History, Elsevier, vol. 87(C).
    12. Bent Flyvbjerg & Alexander Budzier & Jong Seok Lee & Mark Keil & Daniel Lunn & Dirk W. Bester, 2022. "The Empirical Reality of IT Project Cost Overruns: Discovering A Power-Law Distribution," Papers 2210.01573, arXiv.org.
    13. Vlad DIACONITA, 2015. "Approaches for parallel data loading and data querying," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 6(1), pages 78-85, July.
    14. Moritz-C. Schlegel & Claudia Koch & Mona Mirtsch & Andrea Harrer, 2021. "Smart Products Enable Smart Regulations—Optimal Durability Requirements Facilitated by the IoT," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
    15. Chae, Bongsug (Kevin), 2018. "The Internet of Things (IoT): A Survey of Topics and Trends using Twitter Data and Topic Modeling," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190376, International Telecommunications Society (ITS).
    16. Yu-Sheng Kao & Kazumitsu Nawata & Chi-Yo Huang, 2019. "An Exploration and Confirmation of the Factors Influencing Adoption of IoT-Based Wearable Fitness Trackers," IJERPH, MDPI, vol. 16(18), pages 1-31, September.
    17. Roe, Michael & Spanaki, Konstantina & Ioannou, Athina & Zamani, Efpraxia D. & Giannakis, Mihalis, 2022. "Drivers and challenges of internet of things diffusion in smart stores: A field exploration," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    18. Bettina Freitag & Lukas Häfner & Verena Pfeuffer & Jochen Übelhör, 2020. "Evaluating investments in flexible on-demand production capacity: a real options approach," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 133-161, April.
    19. Timon B. Heinis & Jan Hilario & Mirko Meboldt, 2018. "Empirical study on innovation motivators and inhibitors of Internet of Things applications for industrial manufacturing enterprises," Journal of Innovation and Entrepreneurship, Springer, vol. 7(1), pages 1-22, December.
    20. Akhtar, Pervaiz & Khan, Zaheer & Tarba, Shlomo & Jayawickrama, Uchitha, 2018. "The Internet of Things, dynamic data and information processing capabilities, and operational agility," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 307-316.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aes:infoec:v:25:y:2021:i:2:p:64-74. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Paul Pocatilu (email available below). General contact details of provider: https://edirc.repec.org/data/aseeero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.