[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122639-en":3,"doc-seo-122639-105":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},122639,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization","Building accurate and interpretable machine learning models for heterogeneous or mixed data is a persistent challenge for algorithms originally designed for numeric inputs. The study introduces numeric coding schemes for nonnumeric attributes and lossless visualization methods for n-dimensional categorical data, enabling visual rule discovery. It proposes a classification of mixed data types, a toolkit that enforces interpretability across internal ML operations, and a Sequential Rule Generation (SRG) algorithm evaluated in multiple computational experiments. The approach advances explainable ML for mixed data using lossless n-D visualization beyond parallel coordinates.","Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization  \nBoris Kovalerchuk, Elijah McCoy  \nDept. of Computer Science  \nCentral Washington University, USA  \n[Boris.Kovalerchuk@cwu.edu](Boris.Kovalerchuk@cwu.edu), [Elijah.McCoy@cwu.edu](Elijah.McCoy@cwu.edu)  \nAbstract. Building accurate and interpretable Machine Learning (ML) models for heterogeneous/mixed data is a long-standing challenge for algorithms designed for numeric data. This work focuses on developing numeric coding schemes for nonnumeric attributes for ML algorithms to support accurate and explainable ML models, methods for lossless visualization of n-D non-numeric categorical data with visual rule discovery in these visualizations, and accurate and explainable ML models for categorical data. This study proposes a classification of mixed data types and analyzes their important role in Machine Learning. It presents a toolkit for enforcing interpretability of all internal operations of ML algorithms on mixed data with a visual data exploration on mixed data. A new Sequential Rule Generation (SRG) algorithm for explainable rule generation with categorical data is proposed and successfully evaluated in multiple computational experiments. This work is one of the steps to the full scope ML algorithms for mixed data supported by lossless visualization of n-D data in General Line Coordinates beyond Parallel Coordinates.  \nKeywords. Heterogeneous/mixed data, explainable machine learning, lossless visualization, parallel coordinates, rule discovery.  \n1. INTRODUCTION  \nMany Machine Learning (ML) datasets contain mixed/heterogeneous non-numeric data, but multiple ML algorithms cannot discover models on such diverse data [31] . Mixed data include text, graphics, numeric and non-numeric data. Non-numeric data frequently take the form of ordinal (ordered) data, such as large, medium, and tiny, or nominal data with values like red, green, and blue.  \nThe success of explainable ML algorithms for mixed data heavily depends on abilities of lossless visualization of multidimensional data and ML models. The latter enablesend-users, who can contribute valuable domain knowledge that is missing from the training data, to create explainable ML models using visual knowledge discovery [13, 20] .  \nAdditionally, the interpretability and appeal of visual machine learning models are frequently higher for the end user than those of analytical ML models. This creates anew window of opportunity for developing and advancing the field of explainable ML.  \nIt requires to visualize n-D data without loss of n-D information, because it is not known in advance which of this n-D information will be critical for discovering ML model ina visual form.  \nHowever, creating methods for lossless visualization for multiple data types is a longstanding challenge [1,10, 11], because it is more difficult than producing lossy visualizations of high dimensional data, which are less demanding on data preservation.  \nFor instance, [33] focuses on existing lossy methods like 2-D projections and limited Star coordinates with visualizing only end points of the graphs making this visualization lossy relative to n-D data. In general, research on the lossless visualization of mixed nominal, ordinal, and numerical attributes and its contribution to the explainable machine learning is in the nascent stage.  \nNumeric coding is a popular method for adapting existing ML algorithms to nonnumeric data [4-6] . If the features of these non-numeric data, such as value similarities, are corrupted by the coding, it can result in non-explainable ML models. For distancebased algorithms like kNN and others it happened with a common integer coding. Unfortunately, there is little focus on interpretability in the coding literature, eventhough it is a significant barrier to expanding the use of ML with mixed data in the domains with high error costs, like medicine.  \nBuilding Explainable Machine Learning (IML) m","cbCaicdWmszmBPHs","https://ap.wps.com/l/cbCaicdWmszmBPHs","pdf",1346042,1,46,"English","en",105,"# Introduction\n## Lossless visualization for explainable ML\n## Mixed data types and interpretability\n## Numeric coding and its limitations\n## Building explainable models with rule generation","[{\"question\":\"What problem does the paper address in explainable machine learning?\",\"answer\":\"It targets the difficulty of building accurate and interpretable ML models when datasets contain heterogeneous or mixed non-numeric data, which many numeric-focused algorithms cannot handle well.\"},{\"question\":\"How does the paper support interpretability for mixed categorical data?\",\"answer\":\"It combines numeric coding schemes for nonnumeric attributes with lossless visualization of n-dimensional categorical data, enabling visual knowledge discovery and interpretable rule discovery.\"},{\"question\":\"What is the proposed SRG algorithm used for?\",\"answer\":\"The Sequential Rule Generation (SRG) algorithm generates explainable rules for categorical data and is evaluated through multiple computational experiments.\"}]","Explainable Machine Learning for Categorical and Mixed Data with Lossless Visualization | 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problem does the paper address in explainable machine learning?","Question",{"text":75,"@type":76},"It targets the difficulty of building accurate and interpretable ML models when datasets contain heterogeneous or mixed non-numeric data, which many numeric-focused algorithms cannot handle well.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper support interpretability for mixed categorical data?",{"text":80,"@type":76},"It combines numeric coding schemes for nonnumeric attributes with lossless visualization of n-dimensional categorical data, enabling visual knowledge discovery and interpretable rule discovery.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the proposed SRG algorithm used for?",{"text":84,"@type":76},"The Sequential Rule Generation (SRG) algorithm generates explainable rules for categorical data and is evaluated through multiple computational 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