[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-149879-en":3,"doc-seo-149879-105":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},149879,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Visualizing Statistical Models - Removing the Blindfold","Visualization strengthens model building, diagnosis, and understanding of how a statistical model summarizes data. The paper presents three complementary strategies for model visualization: rendering the model in data space, examining all members of a model collection, and exploring the fitting process rather than only the final result. Each strategy is supported with examples spanning multivariate analysis, classification algorithms, clustering, linear model ensembles, projection pursuit, self-organizing maps, and neural networks.","Visualizing statistical models: Removing the blindfold  \nHadley Wickham, Dianne Cook and Heike Hofmann  \nDepartment of Statistics MS-138  \n6100 Main St  \nHouston TX 77081  \ne-mail: [hadley@rice.edu](hadley@rice.edu)  \nDepartment of Statistics  \n2415 Snedecor Hall  \nAmes IA 50011-1210  \ne-mail: [dcook@iastate.edu](dcook@iastate.edu)  \nDepartment of Statistics  \n2413 Snedecor Hall  \nAmes IA 50011-1210  \ne-mail: [hofmann@iastate.edu](hofmann@iastate.edu)  \nAbstract: Visualization can help in model building, diagnosis, and in developing an understanding about how a model summarizes data. This paper proposes three strategies for visualizing statistical models: (1) display the model in the data space,(2) look at all members of a collection, and (3) explore the process of model 􀀌tting, not just the end result. Each strategy is accompanied by examples, including manova, classi􀀌cation algorithms, hierarchical clustering, ensembles of linear models, projection  \npursuit, self organizing maps and neural networks.  \nKeywords and phrases: model visualization, exploratory data analysis, data mining, classi􀀌cation,  \nhigh-dimensional data.  \n1. Introduction  \nVisual methods for high-dimensional data are well developed and understood. Our toolbox contains static graphics, such as scatterplot matrices, parallel coordinate plots and glyphs, interactive tools like brushing and linking, and dynamic methods, such as tours. We can also use these tools to visualize our models, and when we have done so, we have often been surprised: 􀀌tted models can be quite di􀀋erent from what we expect!  \nVisual model descriptions are particularly important adjuncts to numerical summaries because they help answer di􀀋erent types of questions:  \n􀀏 What does the model look like? How does the model change when its parameters change? How do the parameters change when the data is changed?  \n􀀏 How well does the model 􀀌t the data? How does the shape of the model compare to the shape of the data? Is the model 􀀌tting uniformly good, or good in some regions but poor in other regions? Where might the 􀀌t be improved?  \nIf we cannot easily ask and answer these questions, our ability to understand and criticize models is constrained, and our view of the underlying phenomenon 􀀍awed. This may not matter if we only care about accurate predictions, but better understanding of the underlying science usually enhances generalization.  \nAs well as facilitating good science, model visualization (model-vis) can also be used to enhance teaching and research. Pedagogically, it gives students another way of understanding newly encountered methods. Using modern interactive methods students can become experimental scientists studying a model; systematically varying the input and observing the model output. Model-vis can also be useful in the development of new theory; seeing where a model or a class of models performs poorly may suggest avenues for new research.  \nConverting data-vis methods to model-vis methods is not always straightforward. This paper summarizesour experiences, and provides three overarching strategies:  \n􀀏 Display the model in data space (m-in-ds), as well as the data in the model space. It is common to show the data in the model space, for example, predicted vs observed plots for regression, linear discriminant  \nplots, and principal components. By displaying the model in the high-d data space, rather than low-d summaries of the data produced by the model, we expect to better understand the 􀀌t.  \n􀀏 Look at all members of a collection of a model, not just a single one. Exploring multiple models usually gives more insight into the data and the relative merits of di􀀋erent models. This is analogous to the way that studying many local optima give a more complete insight than a single global optimum, orthe way that multiple summary statistics are more informative than one alone.  \n􀀏 Explore the process of 􀀌tting, not just the end result. Understanding how the algorithm works allow","cbCaipLCxnWEwlIS","https://ap.wps.com/l/cbCaipLCxnWEwlIS","pdf",3390427,1,28,"English","en",105,"# Introduction\n## Three strategies for model visualization\n# Background\n## Terminology and recurring visual methods\n# Model-in-data space (m-in-ds)\n## Comparing against data-in-model approaches\n# Visualizing collections of models\n## Ensemble of linear models\n# Visualizing model fitting processes\n## Projection pursuit and self-organizing maps\n# Visualizing neural networks with combined strategies","[{\"question\":\"What is the main goal of model visualization in the paper?\",\"answer\":\"To use visual methods as an adjunct to numerical summaries so people can better understand, diagnose, and critique statistical models and their ability to fit data.\"},{\"question\":\"What are the three strategies proposed for visualizing statistical models?\",\"answer\":\"Display the model in data space, look at all members of a model collection, and explore the model-fitting process rather than only the end result.\"},{\"question\":\"Why is exploring multiple models or collections important?\",\"answer\":\"Studying many related models provides more insight into the data and clarifies the relative merits of different modeling choices, compared with relying on a single best model.\"}]","Visualizing Statistical Models - 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