iVLAIR - Interface Design and Prototype for Interactive Visualization-Mediated Supervised Classification

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dc.contributor.authorKuppusami, Sanchitha
dc.contributor.supervisorNacenta, Miguel
dc.date.accessioned2023-08-31T20:28:07Z
dc.date.available2023-08-31T20:28:07Z
dc.date.copyright2023en_US
dc.date.issued2023-08-31
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractMachine learning is widely used in various applications because of its advantages, but not everyone can apply it effectively as it requires the use of textual programming. To overcome this, there are several approaches that help users perform machine learning classification without writing code. However, for most machine learning models, it is difficult to understand how they arrive at a particular result. This challenge has triggered a lot of research on interpretable ML methods. However, these methods also require the user to learn how to code and implement them. In this work we introduce iVLAIR, a web application tool that eases machine learning classification for a wider audience and makes data more understandable to the users by transforming the data into visualizations, thereby improving the model interpretability. We also conduct an evaluation with machine learning experts and non-experts to compare iVLAIR with the python approach for performing machine learning classification.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/15327
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectvisualizationen_US
dc.subjectinterfaceen_US
dc.subjectml for novicesen_US
dc.subjectno-codeen_US
dc.titleiVLAIR - Interface Design and Prototype for Interactive Visualization-Mediated Supervised Classificationen_US
dc.typeThesisen_US

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