Modern Deep Learning for Tabular Data: Novel Approaches to Common Modeling Problems

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2023

A. Ye, Z. WangModern Deep Learning for Tabular Datahttps://doi.org/10.1007/978-1-4842-8692-0_12

12. Neural Network Interpretability

Andre Ye¹   and Zian Wang²

(1)

Seattle, WA, USA

(2)

Redmond, WA, USA

 

I have always thought the actions of men the best interpreters of their thoughts.

—John Locke, Political Theorist

Neural networks are powerful tools that can be used to solve a host of difficult tabular data modeling challenges. However, they’re also less obviously interpretable than other alternatives to modeling tabular data, like Linear Regression or decision trees – from which the model’s processing of the data can be more or less directly read off of the learned ...