Abstract
Businesses in high-risk environments have been reluctant to adopt modern machine learning approaches due to their complex and uninterpretable nature. Most current solutions provide local, instance-level explanations, but this is insufficient for understanding the model as a whole. In this work, we show that strategy clusters (i.e., groups of data instances that are treated distinctly by the model) can be used to understand the global behavior of a complex ML model. To support effective exploration and understanding of these clusters, we introduce StrategyAtlas, a system designed to analyze and explain model strategies. Furthermore, it supports multiple ways to utilize these strategies for simplifying and improving the reference model. In collaboration with a large insurance company, we present a use case in automatic insurance acceptance, and show how professional data scientists were enabled to understand a complex model and improve the production model based on these insights.
Highlights
W HILE modern machine learning (ML) techniques have great potential to solve a wide spectrum of real-world problems, some businesses have been reluctant to adopt this technology
We present STRATEGYATLAS, a visual analytics system to enable understanding of complex models by identifying and interpreting different model strategies
We present a use case analyzing an operational machine learning model used for automatic acceptance of certain insurance policies
Summary
W HILE modern machine learning (ML) techniques have great potential to solve a wide spectrum of real-world problems, some businesses have been reluctant to adopt this technology. Either the model needs to be inherently interpretable, or the model has to be sufficiently explained using an external method. This need is further exemplified by the surge of papers in which models are shown to be vulnerable to adversarial attacks. In these cases authors show that a small perturbation in the input (e.g., a single pixel in an image) can lead to unexpected, extreme changes in the output, often leading to absurd or incorrect predictions [1, 2]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Visualization and Computer Graphics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
