[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118666-en":3,"doc-seo-118666-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118666,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Applying Machine Learning To Predict Future Milk Production In Dairy Cows - Thesis","Applying machine learning to forecast future milk production in dairy cows supports evidence-driven decision-making for lactation management and cow selection. The thesis first reviews core machine learning foundations, common linear regression methods, and optimization strategies. It then presents “Brisk,” a Python package that simplifies supervised model training for non-computer science users, including motivations, usage guidance, and a hands-on case study. Finally, two predictive models are developed for a decision support system, targeting day-305 milk yield based on performance at 60 days in milk.","Applying Machine Learning To Predict Future Milk Production In Dairy  \nCows  \nby  \nBraeden Fieguth  \nA Thesis  \npresented to  \nThe University of Guelph  \nIn partial fulfilment of requirements  \nfor the degree of  \nMaster of Science  \nin  \nAnimal Biosciences  \nGuelph, Ontario, Canada  \n© Braeden Fieguth , August , 2025  \nAbstract  \nApplying Machine Learning To Predict Future Milk Production In Dairy Cows  \nBraeden Fieguth Advisor(s):  \nUniversity of Guelph, 2025 Dr. John Cant  \nDr. Dan Tulpan  \nDr. Jennifer Ellis  \nDr. Dave Innes  \nThis thesis comprises three main chapters. First, a literature review explores fundamental machine learning concepts, common linear regression algorithms and optimization techniques. Second, it introduces \"Brisk,\" a Python package developed to make machine learning more accessible for non-computer science users, detailing its motivations, usage, and a practical case study. Finally, the thesis presents two machine learning models developed for a decision support system. These models predict day- 305 milk yield at 60 days in milk. These models aim to identify cows suitable for an extended lactation management strategy.  \nAcknowledgements  \nThis thesis would not have been possible without the support of many individuals. I want to express my sincere appreciation to my committee members, Dr. Dan Tulpan and Dr. Jen Ellis, whose thoughtful suggestions and constructive feedback were instrumental in refining my models. My thanks also go to Dr. Lucas Alcantara for his assistance in providing data from the Ontario Dairy Research Centre, which saved countless hours of work. I am grateful to Trouw Nutrition and Dr. Dave Seymour for generously providing additional data from the Kempenshof Dairy Research Facility. A special thank you to Dr. Dave Innes who helped me transition into graduate studies and who generously shared his time and knowledge teaching me a great deal about data science. Above all I am grateful to my advisor, Dr. John Cant , for encouraging me to consider graduate studies and pursue my interests in computer science applications in the animal science field. I could not have picked a better advisor.  \nTable of Contents  \nAbstract ............................................................................................................................ ii  \nAcknowledgements ......................................................................................................... iii  \nTable of Contents ............................................................................................................iv  \nList of Tables ..................................................................................................................vii  \nList of Figures................................................................................................................ viii  \nList of Appendices ...........................................................................................................ix  \n1 Introduction ............................................................................................................... 1  \n2 Literature Review ...................................................................................................... 3  \n2.1 What is Learning? .............................................................................................. 3  \n2.2 The Bias-Variance Tradeoff ............................................................................... 5  \n2.3 Implementing Machine Learning Algorithms ...................................................... 7  \n2.3.1 Ordinary Least Squares Linear Regression ................................................. 7  \n2.3.2 Ridge Regression ........................................................................................ 9  \n2.3.3 LASSO Regression ................................................................................... 13  \n2.4 Closing Statement ..............................................................","cbCaianiqaQO3q2M","https://ap.wps.com/l/cbCaianiqaQO3q2M","pdf",4285073,1,78,"English","en",105,"# Abstract\n# Acknowledgements\n# List of Tables\n# List of Figures\n# List of Appendices\n# 1 Introduction\n# 2 Literature Review\n## 2.1 What is Learning?\n## 2.2 The Bias-Variance Tradeoff\n## 2.3 Implementing Machine Learning Algorithms\n## 3 Brisk: A Framework for Training Supervised Machine Learning Models Using scikit-learn\n## 4 Predicting day-305 milk yield using early lactation milk production","[{\"question\":\"What is the thesis’s main goal?\",\"answer\":\"To apply machine learning to predict future milk production in dairy cows, specifically forecasting day-305 milk yield based on data from 60 days in milk.\"},{\"question\":\"What does the Brisk package contribute?\",\"answer\":\"Brisk is a Python framework designed to make supervised machine learning training more accessible for non-computer science users, including motivations, usage, and a practical case study.\"},{\"question\":\"How are the predictive models intended to be used?\",\"answer\":\"The models support decision-making by identifying cows suitable for an extended lactation management strategy.\"}]","Applying Machine Learning To Predict Future Milk Production In Dairy Cows - 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