[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125184-en":3,"doc-seo-125184-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},125184,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Classification and Evaluation of Machine Learning Algorithms on the MNIST Dataset - read online free","This paper evaluates machine learning methods for classifying the MNIST handwritten digit dataset. MNIST contains 28x28 grayscale images covering 10 classes labeled 0–9, normalized by scaling pixel values to the 0–1 range. The study compares K-nearest neighbor and Naive Bayes using accuracy, error rate, F1-score, and precision. Results show that the K-nearest neighbor algorithm delivers stronger performance across all evaluation criteria, demonstrating better classification effectiveness for this dataset.","University of Central Florida  \nSTARS  \nData Science and Data Mining  \nJanuary 2025  \n# Classification and Evaluation of Machine Learning Algorithms onthe MNIST Dataset\n\nFelix Yeboah  \nUniversity of Central Florida, fe528610@ucf.edu  \n Part of the Data Science Commons  \nFind similar works at: https://stars.library.ucf.edu/data-science-mining  \nUniversity of Central Florida Libraries http://library.ucf.edu  \nThis Article is brought to you for free and open access by STARS. It has been accepted for inclusion in Data Scienceand Data Mining by an authorized administrator of STARS. For more information, please contact STARS@ucf.edu .  \nSTARS Citation  \nYeboah, Felix, \"Classification and Evaluation of Machine Learning Algorithms on the MNIST Dataset\" (2025) . DataScience and Data Mining. 27.  \nhttps://stars.library.ucf.edu/data-science-mining/27  \n# Classifcation and Evaluation of Machine Learning\n\nAlgorithms on the MNIST Dataset  \nFelix Yeboah  \nDepartment of Statistics and Data ScienceUniversity of Central FloridaOrlando, United Statesfe528610@ucf.edu  \nAbstract—This paper discusses the use of machine learningalgorithms in classifying the MNIST handwritten dataset. TheMNIST dataset consists of 28x28 grayscale handwritten imageswith 10 classes from 0 to 9. The dataset was normalized byscaling the pixel values to a range between 0 and 1 by dividingeach pixel value by 255. We compare and evaluate the K-nearestNeighbor and Naive Bayes algorithm based on performancemetrics such as accuracy, error rate, f1-score, and precision.The K-nearest Neighbor algorithm achieved better performancein all the evaluation criteria.  \nIndex Terms—Machine Learning, K-Nearest Neighbor, Preci -sion, Recall, Naive Bayes.  \n## I. INTRODUCTION\n\nRecently, there has been renewed interest in using comput -ers to capture information from visual data (pictures, videos,images, patterns, etc) . The signifcant advancement in theo -retical frameworks and methodologies can be praised for thistechnological breakthrough. This feld of research is a branchof artifcial intelligence known as computer vision. Computervision is human-machine interaction that empowers machinesto input, process, interpret, and understand visual information,improving exchange and interaction between humans and ma -chines [1] . One of the most signifcant sub-felds of computervision is image classifcation, which involves labeling andcategorizing input images, represented as pixels and vectors,into specifc groups or classes based on their visual features[2] . Arguably, image categorization tasks can be generalizedinto two forms, depending on whether the data has givenlabels: supervised learning and unsupervised learning. Theformer is used to train input data when a labeled output isavailable, while the latter is used to train unlabelled data.The importance of image classifcation is indisputable, asit plays a crucial role in advancing decision-making acrossvarious industries. The application of image classifcation isvast; a few examples include its use in healthcare, where itaids in diagnosing diseases through medical imaging [3], andin agriculture, where it assists in identifying plant diseasesand pests, thus optimizing agricultural practices [3] and inautonomous driving, where it enhances object and obstaclesdetection [4] .  \nThis project set out to evaluate and compare the per-formance of two classifcation algorithms on the MNISTdataset. The aim is to determine the effciency, accuracy, and  \ncomputational requirements of the K Nearest Neighbour andNaive Bayes algorithms. Part of this project aims to detail thestrengths and shortcomings of each algorithm in handling thechallenges posed by the MNIST dataset.  \n## II. DATA\n\n### A. Data Description\n\nThe MNIST database (Modifed National Institute of Stan -dards and Technology database) is an extensive database ofhandwritten digits that is commonly used to train variousmachine learning algorithms. The database consists of hand -written digits collected","cbCaie8Yq9K2pttn","https://ap.wps.com/l/cbCaie8Yq9K2pttn","pdf",1888239,1,"English","en",105,"# Classification and Evaluation of Machine Learning\n## Algorithms on the MNIST Dataset\n# Introduction\n# Data\n## Data Description\n## Exploratory Data Analysis","[{\"question\":\"What dataset and preprocessing steps are used for the study?\",\"answer\":\"The study uses the MNIST dataset of 28x28 grayscale handwritten digits with labels 0–9. Pixel values are normalized by scaling each pixel to a 0–1 range by dividing by 255.\"},{\"question\":\"Which machine learning algorithms are compared, and what evaluation metrics are used?\",\"answer\":\"K-nearest neighbor and Naive Bayes are compared. Performance is measured using accuracy, error rate, F1-score, and precision.\"},{\"question\":\"What is the main conclusion about algorithm performance on MNIST?\",\"answer\":\"K-nearest neighbor achieves better performance than Naive Bayes across all evaluation criteria, including accuracy-related and error/score-based metrics.\"}]","Classification and Evaluation of Machine Learning Algorithms on the MNIST Dataset - read online free | PDF",1785897257,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"classification-and-evaluation-of-machine-learning-algorithms-on-the-mnist-dataset-read-online-free","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/classification-and-evaluation-of-machine-learning-algorithms-on-the-mnist-dataset-read-online-free/125184/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What dataset and preprocessing steps are used for the study?","Question",{"text":74,"@type":75},"The study uses the MNIST dataset of 28x28 grayscale handwritten digits with labels 0–9. Pixel values are normalized by scaling each pixel to a 0–1 range by dividing by 255.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning algorithms are compared, and what evaluation metrics are used?",{"text":79,"@type":75},"K-nearest neighbor and Naive Bayes are compared. 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