Max Kuhn - The Post-Modeling Model to Fix the Model

The Post-Modeling Model to Fix the Model by Max Kuhn

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Abstract: It's possible to get a model that has good numerical performance but has predictions that are not really consistent with the data. Model calibration is a tool that can fix this. We'll show some examples of poor predictions and how different calibration tools can re-align them to the data.

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 numerous 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 second book, Feature Engineering and Selection, was published in 2019.

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Presented at the 2023 New York R Conference (July 14, 2023)