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Tree‐Based Models for Political Science Data
Author(s) -
Montgomery Jacob M.,
Olivella Santiago
Publication year - 2018
Publication title -
american journal of political science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.347
H-Index - 170
eISSN - 1540-5907
pISSN - 0092-5853
DOI - 10.1111/ajps.12361
Subject(s) - computer science , tree (set theory) , nonparametric statistics , data science , covariate , big data , data mining , theoretical computer science , family tree , machine learning , mathematics , econometrics , mathematical analysis , combinatorics
Political scientists often find themselves analyzing data sets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in “big data,” as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high‐dimensional data sets. In this article, we introduce a family of tree‐based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in data sets with many (potentially irrelevant) covariates. We introduce the basic logic of tree‐based models, provide an overview of the most prominent methods in the literature, and conduct three analyses that illustrate how the methods can be implemented while highlighting both their advantages and limitations.

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