Model Validation for Applied Data Science -- 2021-11-19

In this workshop, we will discuss the basics of creating, comparing, and validating predictive models using a case study from the health sciences. We will demonstrate categorical prediction with logistic regression, and numerical predictions with a regression tree approach. We will calculate measurements of accuracy that are applicable to the different types of models, and use cross-validation to find the model parameters that generate the best predictions. Finally, we will interpret the results for insights about the real-world process being modeled. While this workshop features working with health data, the conceptual framework and principles discussed should be generalizable to research in other domain. 

This workshop is open to learners at all levels, but prior experience with R is required in order to fully participate in this interactive, hands-on workshop.

Please follow the DataLab install guides (https://datalab.ucdavis.edu/install-guide/) to install R and RStudio before the workshop. 

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