Despite Japan being one of the world's largest advanced democracies, the development of election forecasting models for its national elections remains limited. This study introduces nonlinear machine-learning forecasting models, based on decision tree and ensemble learning methods, for predicting the outcomes of Japanese lower-house elections. To assess the methodological benefits of our approach, we replicated the theoretical framework and dataset of Lewis-Beck and Tien's (LBT) foundational statistical forecasting model for Japanese elections. Our models demonstrated moderately but consistently improved predictive accuracy compared to LBT's model in both in-sample and out-of-sample evaluations, suggesting that nonlinear algorithms offer an alternative approach to classical linear methods in capturing complex electoral dynamics. This study represents one of the earlier applications of nonlinear machine-learning techniques to single-country election forecasting. It offers a replicable framework that, when combined with the country-specific electoral theories of other nations, may enhance the predictive performance of forecasting models in broader national contexts.

翻译：尽管日本是全球最大的先进民主国家之一，但其全国性选举预测模型的发展仍十分有限。本研究引入基于决策树与集成学习方法的非线性机器学习预测模型，用于预测日本众议院选举结果。为评估该方法论优势，我们复现了Lewis-Beck与Tien（LBT）针对日本选举的基础统计预测模型的理论框架与数据集。在样本内与样本外评估中，我们的模型预测精度相比LBT模型均呈现温和但持续提升，表明非线性算法在捕捉复杂选举动态方面为经典线性方法提供了替代方案。本研究是非线性机器学习技术较早应用于单一国家选举预测的案例之一，其提供的可复制框架若与其他国家的特定选举理论相结合，有望在更广泛的国家背景下提升预测模型的性能。