ITM Web Conf.
Volume 14, 2017The 12th International Conference Applied Mathematical Programming and Modelling – APMOD 2016
|Number of page(s)||16|
|Published online||08 November 2017|
Shaking the trees: Abilities and Capabilities of Regression and Decision Trees for Political Science
KDSS Data Science, 1060 Vienna, Austria
2 WU Vienna University of Economics and Business, Welthandelsplatz 1, 1020 Vienna, Austria
* e-mail: email@example.com
** e-mail: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.