Computational Modelling with Visual Analytics

Natalia Andrienko, Gennady Andrienko, Georg Fuchs, Aidan Slingsby, Cagatay Turkay and Stefan Wrobel
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Natalia Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Gennady Andrienko: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Georg Fuchs: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven
Aidan Slingsby: City, University of London, Northampton Square, Department of Computer Science
Cagatay Turkay: University of Warwick, Centre for Interdisciplinary Methodologies
Stefan Wrobel: Fraunhofer Institute Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven

Chapter Chapter 13 in Visual Analytics for Data Scientists, 2020, pp 375-407 from Springer

Abstract: Abstract Data scientists usually aim at building computer models. Computeroriented modelling methods and software tools are developed in statistics, machine learning, data mining, and various specialised disciplines, such as spatial statistics, transportation research, and animal ecology. However, valid and useful computerbased models cannot be obtained by mere application of some modelling software to available data. Modelling requires understanding of the phenomenon that is modelled, the available data, and the model produced by the computer, which means that computer modelling is a process requiring involvement of a human. This chapter formulates the principles of thoughtful model building and includes examples of the use of visual analytics approaches for fulfilling these principles. The examples cover the tasks of feature engineering and selection, iterative model evaluation and progressive improvement, and comparison of different models.

Date: 2020
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DOI: 10.1007/978-3-030-56146-8_13

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