论文标题
适用于美国立法机关两极分化的机器学习算法的比较
A Comparison of Machine Learning Algorithms Applied to American Legislature Polarization
论文作者
论文摘要
我们为测量美国州立法机关两极分化的新颖方法通过三种不同的机器学习算法进行了实验比较。我们的方法严格依赖公共数据源和开源软件。结果表明，与支持向量机和普通最小二乘回归相比，人工神经网络回归在州议会和州参议院立法机关两极分化时的结果是最佳结果。除了我们研究的技术成果外，还评估了更广泛的含义，以强调可访问信息的重要性，以促进公民责任。
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.
