[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125589-en":3,"doc-seo-125589-105":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},125589,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Parallel Coordinates for Discovery of Interpretable Machine Learning Models - Conference paper","This work develops visual knowledge discovery in parallel coordinates to improve interpretable machine learning. The approach makes hypercube and hyperblock concepts clear to end users and introduces the Hyper classifier using mixed and pure hyperblocks. Hyper models are shown to generalize decision trees and to reduce both overfitting and overgeneralization. Multiple settings enable interactive or automatic discovery of overlapping and non-overlapping hyperblocks, with linguistic descriptions of visual patterns. Experiments on UCI ML benchmark data use 10-fold cross validation, and VisCanvas 2.0 is presented, including a new visualization method for incomplete n-D data with missing values.","Parallel Coordinates for Discovery of Interpretable Machine Learning Models  \nDustin Hayes, Boris Kovalerchuk  \nDept. of Computer Science, Central Washington University, [USA. Dustin.Hayes@cwu.edu](USA. Dustin.Hayes@cwu.edu), [BorisK@cwu.edu](BorisK@cwu.edu)  \nAbstract—This work uses visual knowledge discovery in parallel coordinates to advance methods of interpretable machine learning. The graphic data representation in parallel coordinates made the concepts of hypercubes and hyperblocks (HBs) simple to understand for end users. It is suggested to use mixed and pure hyperblocks in the proposed data classifier algorithm Hyper. It is shown that Hyper models generalize decision trees. The algorithm is presented in several settings and options to discover interactively or automatically overlapping or non-overlapping hyperblocks. Additionally, the use of hyperblocks in conjunction with language descriptions of visual patterns is demonstrated. The benchmark data from the UCI ML repository were used to evaluate the Hyper algorithm. It enabled the discovery of mixed and pure HBs evaluated using 10-fold cross validation. Connections among hyperblocks, dimension reduction and visualization have been established. The capability of end users to find and observe hyperblocks, as well as the ability of side-by-side visualizations to make patterns evident, are among major advantages of hyperblock technology and the Hyper algorithm. A new method to visualize incomplete n-D data with missing values is proposed, while the traditional parallel coordinates do not support it. The ability of HBs to better prevent both overgeneralization and overfitting of data over decision trees is demonstrated as another benefit of the hyperblocks. The features of VisCanvas 2.0 software tool that implements Hyper technology are presented.  \nKeywords—Interpretable machine learning, parallel coordinates, hypercube, hyperblock, decision tree, missing data.  \n1. INTRODUCTION  \nAcceptance, interpretability, and comprehensibility of different classifiers are crucial for future Machine Learning (ML) advancements. For many machine learning models this is a very significant challenge due to their black box specifics making these models incomprehensible. Users are reluctant to deploy such models for high-risk, high-stakes decisions. A promising solution to this problem is visual knowledge discovery [5-7, 15, 18, 22] . We outline a parallel coordinates-based visual knowledge discovery approach below that includes supervised learning, data and model visualization, dimensionality reduction, and model simplification.  \nThe supervised classification models are the main topic of this work. Often, developing a reliable interpretable, explainable, and comprehensible ML model necessitates placing the end-user in control of the creation of a model. The end users are frequently  \nsubject matter experts rather than machine learning professionals. Often for them formal ML models are opaque black boxes. The visual knowledge discovery (VKD) methodology makes it possible to identify ML models, explain them for the end users, and put them in charge of model development. This work extends [15] in: (1) visualization method for data with empty values and more elaborated examples, (2) dealing with the large data,(3) in presenting the features of software tool developed to support visual knowledge discovery in parallel coordinates denoted as VisCanvas 2.0, and (4) generalization of hyperblock approach to other general line coordinates.  \nThe suitability of parallel coordinates for visual knowledge discovery is demonstrated in many prior works [1-3, 10, 13, 19-21] . Parallel coordinates accomplish so without losing any of the multidimensional information, and they support interpretability and comprehensibility by using the original attributes, which have clear domain meaning for the domain's end users. However, the use of parallel coordinates for supervised learning as a primary space for actual","cbCainLJqmAq5rfT","https://ap.wps.com/l/cbCainLJqmAq5rfT","pdf",3641667,1,32,"English","en",105,"# Introduction\n## Visual knowledge discovery and interpretability goals\n## Hypercubes and hyperblocks in parallel coordinates\n## Contributions and chapter organization\n# Supervised learning in parallel coordinates\n## Hyper classification for pure and mixed hyperblocks\n## Comparison with decision trees\n## Case study and evaluation","[{\"question\":\"What problem does the work address in interpretable machine learning?\",\"answer\":\"It targets the low acceptance of classifiers with black-box behavior by enabling visual knowledge discovery so end users can understand and guide model creation.\"},{\"question\":\"How does the Hyper approach represent patterns in parallel coordinates?\",\"answer\":\"It uses hypercubes and hyperblocks, represented naturally in parallel coordinates, and supports discovering overlapping or non-overlapping hyperblocks in interactive or automatic modes.\"},{\"question\":\"What data limitations does the document claim to handle better than traditional parallel coordinates?\",\"answer\":\"It proposes a new method to visualize incomplete n-D data with missing values, which traditional parallel coordinates do not support.\"}]","Parallel Coordinates for Discovery of Interpretable Machine Learning Models - 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