Shaking the trees: Abilities and Capabilities of Regression and Decision Trees for Political Science

When committing to quantitative political science, a researcher has a wealth
of methods to choose from. In this paper we compare the established method of analyzing
roll call data using W-NOMINATE scores to a data-driven supervised machine learning
method: Regression and Decision Trees (RDTs). To do this, we defined two scenarios,
one pertaining to an analytical goal, the other being aimed at predicting unknown voting
behavior. The suitability of both methods is measured in the dimensions of consistency,
tolerance towards misspecification, prediction quality and overall variability. We find that
RDTs are at least as suitable as the established method, at lower computational expense
and are more forgiving with respect to misspecification.