Using Visual Analytics to Interpret Predictive Machine Learning Models

2016-06-17 · Josua Krause, Adam Perer, Enrico Bertini

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.

📄 PDF Abstract BibTeX arXiv:1606.05685

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