Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sreelakshmi, P.; Basavaraj, Santosh; Josephine, V. L. Helen Title A name given to the resource Enhancing the Operational Efficiency of FPOs by Using Predictive Model Building in Machine Learning Through Feature Selection Method Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Green Innovations in Computational Intelligence: Sustainable Strategies and Emerging Technologies;pp.159-187 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394383658.ch8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394383658.ch8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023235995?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023235995?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sreelakshmi P., School of Business and Management (SBM), Christ University, Bangalore, India; Basavaraj S., School of Business and Management (SBM), Christ University, Bangalore, India; Josephine V.L.H., School of Business and Management (SBM), Christ University, Bangalore, India Description An account of the resource This study employs a machine learningbased feature selection approach to identify key factors that significantly enhance the predictive accuracy of models assessing farmer producer organization (FPO) performance. It focused on an FPO in the Anantapur and Kadapa districts of Andhra Pradesh, India; the research investigated the operational effectiveness of small and marginal farmers who are members by the end of 2022. The study reviewed existing shortcomings and proposed necessary policy amendments to ensure the sustainability of FPOs. The study utilized a survey design method, with the exploratory questionnaire through the field study. A sample of 73 small-scale producer farmers from the region was selected for the analysis. The result indicated that a) the reason for the operational efficiency of FPOs, b) the gradient boosting regressor achieved the highest testing accuracy of 0.78 to predict the best feature for FPO performance, and c) FPO insights for policy guidelines. 2025 Scrivener Publishing LLC. Subject The topic of the resource feature selection method; FPO operational efficiency; FPO policy inputs; FPO shortcomings; predictive power Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139438365-8; 978-139438361-0; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online