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Visualizing statistical models: Removing the blindfold
Author(s) -
Wickham Hadley,
Cook Dianne,
Hofmann Heike
Publication year - 2015
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11271
Subject(s) - computer science , projection pursuit , cluster analysis , visualization , artificial intelligence , data mining , projection (relational algebra) , process (computing) , machine learning , statistical model , algorithm , operating system
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: (i) display the model in the data space, (ii) look at all members of a collection, and (iii) explore the process of model fitting, not just the end result. Each strategy is accompanied by examples, including manova , classification algorithms, hierarchical clustering, ensembles of linear models, projection pursuit, self‐organizing maps, and neural networks.