Master Thesis MSTR-2019-75

BibliographyHeyen, Frank: Visual Parameter Space Analysis for Classification Models.
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Master Thesis No. 75 (2019).
55 pages, english.
Abstract

We present a batch training and visualization system that enables users to visually compare different classifiers and parameter configurations in their performance and behavior. Our approach is plugin-based and classifier-agnostic and allows users to add their own datasets and classifier implementations. It provides multiple visualizations, including a multivariate ranking, a similarity map, a scatterplot that shows correlations between parameters and scores, as well as a training history chart. We enable users to interactively filter, highlight, colorize, sort, and group the displayed data. Using an iterative process, we developed our approach over the course of six months in cooperation with domain experts who apply machine learning for natural language processing. Our evaluation consists of two pair analytics studies and a survey with students. It demonstrates the effectiveness and usability of the implementation and shows desire to use it from domain experts, teachers and students.

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Department(s)University of Stuttgart, Institute of Visualisation and Interactive Systems, Visualisation and Interactive Systems
Superviser(s)Sedlmair, Jun.-Prof. Michael; Vu, Prof. Ngoc Thang; Munz, Tanja
Entry dateFebruary 19, 2020
   Publ. Computer Science