[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117851-en":3,"doc-seo-117851-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},117851,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","STRESSOR - An R Package for Benchmarking Machine Learning Models","The stressor package targets the programming overhead that can block discipline-specific researchers from running machine-learning benchmarking workflows. It provides an R interface to Python’s PyCaret to automatically tune and train 15–20 machine learning models for accuracy comparisons, using reticulate to bridge R and Python. stressor also includes synthetic data generation and data thinning, plus cross-validation variants for stress-testing extrapolation and prediction quality under small sample sizes. The workflow supports classification and regression benchmarking on agricultural datasets in about four lines of R code with low laptop computational cost, enabling rapid model accuracy comparisons with minimal programming.","Utah State University  \nDigitalCommons@USU  \n\n| All Graduate Theses and Dissertations | Graduate Studies |\n| --- | --- |\n| 8-2023\u003Cbr>Stressor: An R Package for Benchmarking Machine Models\u003Cbr>Samuel A. Haycock Utah State University\u003Cbr>Follow this and additional works at: [https://digitalcommons.usu.edu/etd](https://digitalcommons.usu.edu/etd)\u003Cbr> Part of the Mathematics Commons, and the Statistical Models Commons | Learning |\n\nRecommended Citation  \nHaycock, Samuel A., \"Stressor: An R Package for Benchmarking Machine Learning Models\" (2023) . All Graduate Theses and Dissertations. 8819.  \n[https://digitalcommons.usu.edu/etd/8819](https://digitalcommons.usu.edu/etd/8819)  \nThis Thesis is brought to you for free and open access by the Graduate Studies at DigitalCommons@USU. It has been accepted for inclusion in All Graduate Theses and Dissertations by an authorized administrator of DigitalCommons@USU. For more information, please [contact digitalcommons@usu.edu](contact digitalcommons@usu.edu).  \nSTRESSOR: AN R PACKAGE FOR BENCHMARKING MACHINE LEARNING  \nMODELS  \nby  \nSamuel A. Haycock  \nA thesis submitted in partial fulfillment  \nof the requirements for the degree  \nof  \nMASTER OF SCIENCE  \nin  \nStatistics  \nApproved:  \nBrent Thomas, Ph.D. D. Richard Cutler, Ph.D.  \nCommittee Member Vice Provost of Graduate Studies  \nUTAH STATE UNIVERSITY  \nLogan, Utah  \nii  \nCopyright © Samuel A. Haycock 2023  \nAll Rights Reserved  \niii  \nABSTRACT  \nstressor: An R Package for Benchmarking Machine Learning Models  \nby  \nSamuel A. Haycock, Master of Science  \nUtah State University, 2023  \nMajor Professor: Brennan Bean, Ph.D.  \nDepartment: Mathematics and Statistics  \nThe programming overhead required to implement machine learning workflows can create a barrier for many discipline-specific researchers with limited programming experience. The stressor package provides an R interface to Python’s PyCaret package, which automatically tunes and trains 15-20 machine learning models for use in accuracy comparisons. This requires the use of the reticulate package, a package that bridges the gap between R and Python. stressor also contains synthetic data generation and data thinning algorithms, along with variants of cross validation, designed to stress-test the ability of machine-learning models to extrapolate and/or provide useful predictions with small sample sizes. The simplicity of stressor allows the user to run cross-validation and its variations with a single line of code after training the models. We show the utility of stressor on two agricultural datasets, one for classification and another for regression. Full benchmarking workflows can be completed in only four lines of code, with relatively small computational cost for intermediate sized datasets on a laptop computer. The results, and more importantly the workflow, provide a template for how applied researchers can quickly generate accuracy comparisons of many machine learning models with very little programming.  \n(84 pages)  \niv  \nPUBLIC ABSTRACT  \nstressor: An R Package for Benchmarking Machine Learning Models  \nSamuel A. Haycock  \nMany discipline specific researchers need a way to quickly compare the accuracy of their predictive models to other alternatives. However, many of these researchers are not experienced with multiple programming languages. Python has recently been the leader in machine learning functionality, which includes the PyCaret library that allows users to develop high-performing machine learning models with only a few lines of code. The goal of the stressor package is to help users of the R programming language access the advantages of PyCaret without having to learn Python. This allows the user to leverage R’s powerful data analysis workflows, while simultaneously leveraging Python’s powerful machine learning functionality. stressor also implements a series of synthetic data set generation functions that create data sets where users can test ideas with models they creat","cbCaik7NArvNICgN","https://ap.wps.com/l/cbCaik7NArvNICgN","pdf",3192048,1,85,"English","en",105,"# Abstract\n# Public Abstract\n# Acknowledgments\n# Acronyms\n# Discussion\n# Met","[{\"question\":\"What problem does the stressor R package address?\",\"answer\":\"It reduces the programming overhead involved in implementing machine-learning workflows, especially for researchers with limited programming experience across multiple languages.\"},{\"question\":\"How does stressor connect R with Python machine learning tools?\",\"answer\":\"It uses the reticulate package to provide an R interface to Python’s PyCaret library, enabling automatic tuning and training of many models.\"},{\"question\":\"What methods does stressor include for stress-testing models?\",\"answer\":\"It provides synthetic data generation, data thinning algorithms, and variants of cross-validation designed to stress-test extrapolation and predictive performance with small sample sizes.\"}]","STRESSOR - 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