Patrick Hall

Patrick Hall

Senior Director of Product at


Practical Techniques for Interpretable Machine Learning


Transparency, auditability, and stability of predictive models and results are typically key differentiators in effective machine learning applications. Patrick will share tips and techniques learned through implementing interpretable machine learning solutions in industries like financial services, telecom, and health insurance. Using a set of publicly available and highly annotated examples, he teaches several holistic approaches to interpretable machine learning. The examples use the well-known University of California Irvine (UCI) credit card dataset and popular open source packages to train constrained, interpretable machine learning models and visualize, explain, and test more complex machine learning models in the context of an example credit-risk application. Along the way, Patrick draws on his applied experience to highlight crucial success factors and common pitfalls not typically discussed in blog posts and open source software documentation, such as the importance of both local and global explanation and the approximate nature of nearly all machine learning explanation techniques.

Who is this presentation for?

Researchers, scientists, data analysts, predictive modelers, business users and other professionals, and anyone else who uses or consumes machine learning techniques

Prerequisite knowledge

A working knowledge of Python, widely used linear modeling approaches, and machine learning algorithms.

Materials or downloads needed in advance

A laptop with a recent version of the Firefox or Chrome browser installed. (This tutorial will use an Aquarium environment.) As a backup, tutorial materials are available on GitHub:

What you'll learn

The audience will learn several practical machine learning interpretability techniques and how to use them with Python. They will also learn the best way to use these techniques and common pitfalls to avoid when applying them.

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