Crunch, Data Engineering and Analytics Conference, October 29-31, 2018 Budapest
Patrick Hall

Patrick Hall

Senior Director of Product at H2O.ai

Bio:

Patrick Hall is senior director for data science products at H2O.ai where he focuses mainly on model interpretability and model management. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning. Prior to joining H2O.ai, Patrick held global customer facing roles and research and development roles at SAS Institute.


Talk:

Toward Human-Centered Machine Learning

Topics:
data science
privacy
interpretability
accuracy
machine learning
Level:
Advanced

Machine learning systems are used today to make life-altering decisions about employment, bail, parole, and lending. Moreover, the scope of decisions delegated to machine learning systems seems likely only to expand in the future. Unfortunately serious discrimination, privacy, and even accuracy concerns can be raised about these systems. Many researchers and practitioners are tackling disparate impact, inaccuracy, privacy violations, and security vulnerabilities with a number of brilliant, but often siloed, approaches. This presentation illustrates how to combine innovations from several sub-disciplines of machine learning research to train explainable, fair, trustable, and accurate predictive modeling systems. Together these techniques can create a new and truly human-centered type of machine learning suitable for use in business- and life-critical decision support.

Workshop:

Practical Techniques for Interpretable Machine Learning

Topics:
machine learning
data science
prediction
interpretability
h2o
python
Level:
Intermediate

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: https://github.com/jphall663/interpretable_machine_learning_with_python

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.