2016 ICML Workshop on Human Interpretability in Machine Learning
Using Visual Analytics to Interpret Predictive Machine Learning Models
Josua Krause, Adam Perer, Enrico Bertini
Abstract
It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing predictive quality. We identify a space of possible solutions and provide two examples of where such techniques have been successfully used in practice.