Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

Interpretable machine learning products

Mike Lee Williams (Cloudera Fast Forward Labs)
11:00am11:40am Wednesday, March 7, 2018
Data science and machine learning
Location: LL20 A Level: Intermediate
Average rating: ****.
(4.86, 7 ratings)

Who is this presentation for?

  • Data scientists and product managers

Prerequisite knowledge

  • A basic understanding of supervised machine learning

What you'll learn

  • Learn how interpretability helps you to trust a model to perform well, explain decisions and change outcomes, and satisfy regulators that the model is safe and nondiscriminatory
  • Explore LIME, a new model-agnostic interpretability tool that allows you to construct explanations for each decision made by an arbitrary model


Interpretable models result in more accurate, safer, and more profitable machine learning products. A model you can interpret and understand is one you can more easily improve and can offer insights that can be used to change real-world outcomes for the better. It is also one you, regulators, and society can better trust to be safe and nondiscriminatory. But interpretability can be hard to ensure. There is a central tension between accuracy and interpretability: the most accurate models are necessarily the hardest to understand.

Michael Lee Williams explores the growing business case for interpretability and its concrete applications, including churn, finance, and healthcare. Along the way, Michael offers an overview of the open source, model-agnostic tool LIME, which gets around the accuracy-interpretability tension by allowing you to peer inside black-box models. Michael concludes with a demonstration of a working web application that uses LIME to explain why customers churn and raises the possibility of intervening to prevent their loss.

Photo of Mike Lee Williams

Mike Lee Williams

Cloudera Fast Forward Labs

Mike Lee Williams is a research engineer at Cloudera Fast Forward Labs, where he builds prototypes that bring the latest ideas in machine learning and AI to life and helps Cloudera’s customers understand how to make use of these new technologies. Mike holds a PhD in astrophysics from Oxford.

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