Foundations of Machine Learning and Regression Methods for Categorical Outcomes



Next course


Further and more detailed information, including the schedule, can be found in the current course tables in the syllabus of the respective course, if the course is offered in the next sessions. The following text serves as information on what can be expected in terms of content in the course.

By nature or by measurement, dependent variables of interest to social and behavioral scientists are frequently categorical. Outcomes that include several ranked or unranked, non- continuous categories–like vote choice, social media platform preference, brand loyalty, and/or condom use—are often of interest, with scientists expressly interested in developing models to explain or classify variation therein. Explanatory models are process-focused, and aim to determine the individual impact of factors that contribute to a particular outcome, often based on a priori theory—e.g., “How does social class affect whether an individual voted for the Conservatives in 2019?”; classification models, alternatively, are outcome-focused, and aim to identify the set of factors that most accurately classify (or predict) a particular outcome—e.g., “How do the Tories best use information from polls, geography, weather, Twitter feeds, and/or social demographics to predict who voted Conservative in 2019?” Chances are your research involves a categorical outcome—binary, ordinal, or multinomial— and thus options thus abound for the modeling approach(es) you might take to address your research question of interest. This course is designed to provide an overview of a number of parametric and non-parametric approaches to exploring your outcome of interest, via both explanatory and classification perspectives. At the end of this course, you should have a clear understanding as to which types of models and methods are available to answer different research questions, and also have experience applying a varied toolkit of these models.