Model-Agnostic Visual Explanation of Machine Learning Models Based on Heat Map

Sawada, Shoko; Toyoda, Masashi

Model-Agnostic Visual Explanation of Machine Learning Models Based on Heat Map

Files

037-039.pdf (341.33 KB)

Date

2019

Authors

Sawada, Shoko
Toyoda, Masashi

Publisher

The Eurographics Association

Abstract

It is essential to assess the trustworthiness of the machine learning models when deploying them to real-world applications, such as healthcare and risk management, in which domain experts need to make critical decisions. We propose a visual analysis method for supporting domain experts to understand and improve a given machine learning model based on a model-agnostic interpretable explanation technique. Our visualization method provides a heat map matrix as an overview of the model explanation and helps efficient feature engineering and data cleaning. We demonstrate our visualization method on a text classification task.

        @inproceedings{10.2312:eurp.20191140
,
booktitle = {EuroVis 2019 - Posters
},
editor = {Madeiras Pereira, João and Raidou, Renata Georgia
},
title = {{Model-Agnostic Visual Explanation of Machine Learning Models Based on Heat Map
}},
author = {Sawada, Shoko and 
Toyoda, Masashi
},
year = {2019
},
publisher = {The Eurographics Association
},
ISBN = {978-3-03868-088-8
},
DOI = {10.2312/eurp.20191140
}
}