Why Data Scientists Prefer Glassbox Machine Learning

Abstract

Recent research has shown that interpretable machine learning models can be just as accurate as blackbox learning methods on tabular datasets. In this tutorial we will walk you through leading open source tools for glassbox learning, and show how intelligible machine learning helps practitioners uncover flaws in their datasets, discover new science, and build models that are more fair and robust. We'll begin with an introduction to the science behind glassbox modeling, and walk through a series of case-studies that highlight the added value of interpretable methods in a variety of domains such as finance and healthcare without compromising accuracy. We'll also show how glassbox models can be used for state of the art differentially private learning, bias detection/mitigation, and how these models can be edited to remove undesirable effects with GAMChanger. We'll also discuss how to train interpretable models with deep neural nets.

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