This course provides an introduction to supervised statistical learning techniques such
as decision trees, random forests and boosting and discusses their potential application
in the social sciences. These methods focus on predicting an outcome Y based on
some learned function f(X) and therefore facilitate new research perspectives in
comparison with traditional regression models, which primarily focus on causation.
Predictive methods also provide a valuable extension to the empirical social scientists’
toolkit as new data sources become more prominent. In addition to introducing
supervised learning methods, the course will include practical sessions to demonstrate
how to tune and evaluate prediction models using the statistical programming language
R.