Papers › A Comparison of Machine Learning Algorithms Applied to American Legislature Polarization
A Comparison of Machine Learning Algorithms Applied to American Legislature Polarization
Gabriel Mersy, Vincent Santore, Isaac Rand, Corrine Kleinman, Grant Wilson, Jason Bonsall, Tyler Edwards
We present a novel approach to the measurement of American state legislature polarization with an experimental comparison of three different machine learning algorithms. Our approach strictly relies on public data sources and open source software. The results suggest that artificial neural network regression has the best outcome compared to both support vector machine and ordinary least squares regression in the prediction of both state House and state Senate legislature polarization. In addition to the technical outcomes of our study, broader implications are assessed as a means of highlighting the importance of accessible information for the higher purpose of promoting civic responsibility.
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