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Monday, June 27 • 10:30am - 12:00pm
Never Tell Me the Odds! Machine Learning with Class Imbalances (Part 2)

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This tutorial will provide an overview of using R to create effective predictive models in cases where at least one class has a low event frequency. These types of problems are often found in applications such as: click through rate prediction, disease prediction, chemical quantitative structure - activity modeling, network intrusion detection, and quantitative marketing. The session will step through the process of building, optimizing, testing, and comparing models that are focused on prediction. A case study is used to illustrate functionality.

For details, refer to tutorial description.

avatar for Max


principal software engineer, Posit PBC
Max Kuhn is a software engineer at Posit PBC where he is working on improving R’s modeling capabilities and maintaining about 30 packages, including caret and tidymodels. He has a Ph.D. in Biostatistics. Max was a Senior Director of Nonclinical Statistics at Pfizer Global R&D and... Read More →

Monday June 27, 2016 10:30am - 12:00pm PDT
Econ 140