In today's rapidly evolving research landscape, machine learning has emerged as an indispensable tool, enabling unparalleled insights and efficiencies across diverse domains. Social scientists are studying the implications of the widespread use of machine learning for society, but they have also begun to use machine learning tools in their own research. This workshop introduces machine learning with R using the tidymodels framework, a collection of packages for predictive modeling using tidyverse principles. We will build, evaluate, compare, and tune predictive models. Thereby, we will start with classic models (e.g., linear, or logistic regression) but also discuss models that are particularly useful for the predictive context, which often involves many variables (e.g., random forests, LASSO). Along the way, we'll learn about key concepts in machine learning, including training, validation, and test data, overfitting, resampling, and feature engineering. Participants will gain knowledge about good predictive modeling practices, as well as hands-on experience using tidymodels packages like parsnip, rsample, recipes, yardstick, tune, and workflows. Participants will also learn how to access Python-based models from within R. Finally, we will discuss how we can visualize and evaluate both the accuracy and fairness of ML models.

Regular R users who are interested in learning about machine learning concepts and models and using machine learning for their own research.

By the end of the course, participants will