Creating model en mass with workflowsets

For our second July event, we are excited to host Max Kuhn of RStudio who will be presenting on the workflowsets package.

This event will be hosted over Zoom, and the link will be available on this Meetup page at noon on the day of the event.

Agenda (times approximate):
7-7:05 pm: R-Ladies NYC Announcements
7:05-7:50: "Creating model en mass with workflowsets"
7:50-8:15: Questions and wrap-up

Abstract: The tidymodels packages allow users to create and optimize models and their pre-processing steps (e.g. PCA, feature encodings, etc). With new data, it is common to try a variety of models to determine what works best. The workflowsets package enables users to easily evaluate, rank, and even combine (with the stacks package) myriad combinations of models and pre-processors.

Bio: Max Kuhn is a software engineer at RStudio. He is currently working on improving R's modeling capabilities. He was a Senior Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics.

Max is the author of R packages for techniques in machine learning and reproducible research. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their latest book, Feature Engineering and Selection, was published in 2019.

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