[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125480-en":3,"doc-seo-125480-105":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":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},125480,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Impact of Missing Values in Machine Learning - A Comprehensive Analysis","Machine learning models rely on high-quality datasets, yet real-world data is frequently affected by missing values that undermine performance and generalization. This paper analyzes the types, causes, and consequences of missingness across ML workflows, covering biased inferences, reduced predictive power, and added computational burdens. It evaluates handling approaches such as imputation and removal, and examines effects on evaluation metrics, cross-validation, and model selection, supported by case studies and real-world examples, with attention to ethical transparency.","DOI: 10.32604/cmc.202x.0xxxxx  \nARTICLE  \nImpact of Missing Values in Machine Learning: A Comprehensive Analysis  \nAbu-fuad Ahmad1, Md Shohel Sayeed2*, KhaznahAlshammari3 and Istiaque Ahmed4  \n1,3New Mexico State University, Las Cruces NM 88001, USA  \n2Multimedia University, Melaka 75450, Malaysia  \n4Osaka Metropolitan University, Osaka 558-8585, Japan  \n*Corresponding Author: Md Shohel Sayeed. Email: [shohel.sayeed@mmu.edu.my](shohel.sayeed@mmu.edu.my)  \n[Received:](Received:) XXXX Accepted: XXXX  \nABSTRACT  \nMachine learning (ML) has become a ubiquitous tool across various domains of data mining and big data analysis. The efficacy of ML models depends heavily on high-quality datasets, which are often complicated by the presence of missing values. Consequently, the performance and generalization of ML models are at risk in the face of such datasets. This paper aims to examine the nuanced impact of missing values on ML workflows, including their types, causes, and consequences. Our analysis focuses on the challenges posed by missing values, including biased inferences, reduced predictive power, and increased computational burdens. The paper further explores strategies for handling missing values, including imputation techniques and removal strategies, and investigates how missing values affect model evaluation metrics and introduces complexities in cross-validation and model selection. The study employs case studies and real-world examples to illustrate the practical implications of addressing missing values. Finally, the discussion extends to future research directions, emphasizing the need for handling missing values ethically and transparently. The primary goal of this paper is to provide insights into the pervasive impact of missing values on ML models and guide practitioners toward effective strategies for achieving robust and reliable model outcomes.  \nKEYWORDS  \nMachine Learning; Data Mining; missing values  \n1 Introduction  \nMachine learning, a term widely used across diverse fields, lacks a universally agreed-upon definition due to its broad applicability and the diverse contributions of researchers from various disciplines [1]–[3] This ambiguity is rooted in the extensive areas it covers and the collaborative efforts of researchers with diverse backgrounds. In a broad sense, machine learning can be understood as an algorithmic framework facilitating data analysis, inference, and the establishment of preliminary functional relationships.  \nThis work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nx CMC, 202X  \nThe roots of machine learning (ML) can be traced back to the scientific community's interest in the 1950s and 1960s in replicating human learning through computer programs. ML is characterized by its ability to learn from data, improving its performance over time. It extracts knowledge from data for prediction and generating new information, reducing uncertainty by offering guidance on problem-solving, especially in tasks lacking explicit instructions for an analytic solution. ML has proven particularly valuable in image and voice processing, pattern recognition, and complex classification tasks, gaining popularity in both academia and industry due to its superior performance with complex and large-scale data [4]–[9] .  \nML has become a cornerstone in decision-making across various applications, including healthcare, finance, and environmental monitoring. Its transformative potential lies in discerning patterns, making predictions, and providing insights based on data. The reliability and efficacy of ML models depend on the quality of the datasets used for training and evaluation. However, the pervasive challenge of missing values in datasets, stemming from various sources, necessitates a comprehensive understanding of their impact on ML models. Datasets form t","cbCaihgnxFmzqj2z","https://ap.wps.com/l/cbCaihgnxFmzqj2z","pdf",1481187,1,15,"English","en",105,"# Introduction\n## Missing values in real-world datasets\n## Objectives and paper structure\n# Types and causes of missing values\n# Strategies for handling missing values\n# Effects on model evaluation and validation","[{\"question\":\"Why do missing values threaten machine learning performance and generalization?\",\"answer\":\"Missing values can distort learned patterns and introduce bias, leading to reduced predictive power and poorer generalization, especially when missingness is not random.\"},{\"question\":\"What categories of missing value handling strategies does the paper discuss?\",\"answer\":\"The paper covers imputation techniques and removal strategies, analyzing how each approach affects downstream ML workflows and model behavior.\"},{\"question\":\"How do missing values influence model evaluation and selection?\",\"answer\":\"Missing values can change evaluation metrics and add complications in cross-validation, which in turn affects model selection reliability.\"}]","Impact of Missing Values in Machine Learning - A Comprehensive Analysis | PDF",1785899238,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"impact-of-missing-values-in-machine-learning-a-comprehensive-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/impact-of-missing-values-in-machine-learning-a-comprehensive-analysis/125480/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do missing values threaten machine learning performance and generalization?","Question",{"text":75,"@type":76},"Missing values can distort learned patterns and introduce bias, leading to reduced predictive power and poorer generalization, especially when missingness is not random.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What categories of missing value handling strategies does the paper discuss?",{"text":80,"@type":76},"The paper covers imputation techniques and removal strategies, analyzing how each approach affects downstream ML workflows and model behavior.",{"name":82,"@type":73,"acceptedAnswer":83},"How do missing values influence model evaluation and selection?",{"text":84,"@type":76},"Missing values can change evaluation metrics and add complications in cross-validation, which in turn affects model selection reliability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]