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.
The copyright on this video is owned by the Regents of the University
of California and is licensed for reuse under the Creative Commons Attribution
4.0 International (CC BY 4.0) License.
