Presentation to the Northwestern University Department of Psychology
Whereas the classic statistical methods (i.e., inferential models) traditionally used in the social and behavioral sciences emphasize interpretability and statistical significance, machine learning methods (i.e., predictive models) emphasize complexity and prediction accuracy. Machine learning methods are thus particularly well-suited for applications where (1) there are nonlinear and complex relationships among many predictor variables and (2) accurately predicting the outcome variable is more important than fully understanding the relationships between variables. This one-hour talk will provide a conceptual introduction to the topic of machine learning (specifically supervised predictive modeling) aimed at providing the audience with an understanding of the relevant concepts and intuitions behind the typical machine learning workflow. Examples will emphasize applications in psychology/psychiatry.