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 Conference Papers Conference Paper Faculty Publications- Conference Papers 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 Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML Subject The topic of the resource Decision Tree Regressor; Ensemble Learning; Gradient Boosting Regressor; Hyperparameter tuning; Outlier Analysis; R-squared Score; Random Forest Regressor Description An account of the resource The agricultural sector's increasing reliance on technology has paved the way for advanced data-driven methodologies, with crop yield prediction emerging as a critical focus. This study dives into the complex landscape of crop yield prediction, employing a comprehensive approach that involves data preprocessing, model development, and performance evaluation. This research goes into enhancing crop yield prediction through a thorough data-driven approach. Beginning with comprehensive data preprocessing, including outlier analysis and feature scaling, the study ensures dataset integrity. Ensemble learning, employing Gradient Boosting Regressor, Random Forest Regressor and Decision Tree Regressor, captures intricate relationships within the dataset. Model performance, assessed through R-squared scores, demonstrates promising predictive capabilities. Subsequent outlier analysis and hyperparameter tuning yield substantial improvements, contributing valuable insights for agricultural decision-making. The research not only advances crop yield prediction but also offers practical guidance for integrating machine learning into agriculture, promising transformative outcomes for sustainable practices. The research also highlights how significant interpretability is to machine learning models so that stakeholders can comprehend and embrace them. This allows for a smooth integration of the models into current agricultural practices and encourages openness and reliability in decision-making. 2024 IEEE. Creator An entity primarily responsible for making the resource Upreti K.; Lingareddy N.; Deepika S.; Kumar N.; Parashar J.; Divakaran P. Source A related resource from which the described resource is derived Proceedings - 2024 1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024, pp. 431-436. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/TIACOMP64125.2024.00078" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/TIACOMP64125.2024.00078</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212043398&amp;doi=10.1109%2FTIACOMP64125.2024.00078&amp;partnerID=40&amp;md5=80771159abaa3f5e48b09bb5179a808d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85212043398&amp;doi=10.1109%2fTIACOMP64125.2024.00078&amp;partnerID=40&amp;md5=80771159abaa3f5e48b09bb5179a808d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835039211-1 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upreti K., Christ (Deemed to Be University), Dept. of Computer Science, Delhi NCR, India; Lingareddy N., Vignan Institute of Technology and Science, Department of Cse, Telangana, India; Deepika S., Sreyas Institute of Engineering and Technology, Department of Cse, Telangana, Hyderabad, India; Kumar N., Christ (Deemed to Be University), School of Business and Management, Bangalore, India; Parashar J., Adgitm, Dept. of Computer Science &amp; Engg, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India