Forecasting political voting: A high dimensional machine learning approach

Abstract

We present a novel machine learning approach to predict voting patterns in Brazil’s Chamber of Deputies. Using a high-dimensional dataset and a time-series methodology, our models aim to accurately forecast legislative decisions. Unlike prior studies that often focus on single ideological dimensions, our approach integrates a broad feature set, including party guidelines, proposition characteristics, and deputy voting history, to improve predictive power. We train time-series models for each legislature, comparing ensembles like Random Forests and Gradient Boosting, which are validated using three-fold chronological splits to ensure temporal integrity. Our analysis highlights the significant influence of party guidelines and pork-barrel politics on voting behavior. Additionally, we identify key predictors, including the theme and source of the legislative proposition, as well as the deputies’ voting history. This work demonstrates the feasibility of accurately forecasting legislative votes, offering a valuable tool for stakeholders to anticipate legislative outcomes and enhancing the transparency of the political process.