Ensemble Machine Learning Methods for Predicting Supreme Court Decisions

Published: 7 June 2024| Version 1 | DOI: 10.17632/xfn9n3m9hk.1
Bola Abimbola


This study investigates the efficacy of ensemble machine learning methods in predicting U.S. Supreme Court decisions. By addressing critical research questions, we employ a comprehensive methodology that combines various machine learning techniques to analyze and predict judicial outcomes. Our main findings demonstrate the potential of these advanced algorithms in improving the accuracy of predictions compared to traditional methods. The implications of this research are significant for legal scholars, practitioners, and policymakers, providing a robust tool for legal analytics and decision-making. Predicting Supreme Court decisions holds significant importance in the legal field, as it can provide insights into judicial behavior, inform legal strategies, and shape policy decisions. The advent of machine learning has revolutionized various domains, including legal analytics, by offering sophisticated tools for data analysis and prediction. Applying these tools to Supreme Court decisions can enhance our understanding of judicial trends and decision-making processes. Despite advancements in machine learning, accurately predicting Supreme Court decisions remains a challenging task. Existing models often need to account for the complex factors influencing judicial decisions. This research addresses these gaps by developing and evaluating ensemble machine-learning methods that integrate multiple predictive algorithms to improve accuracy and reliability. This research is pivotal for several reasons. For legal scholars, it offers a deeper understanding of judicial behavior and decision-making patterns. Practitioners can leverage these insights to devise more effective legal strategies, while policymakers can use the findings to anticipate and respond to judicial trends. Applying ensemble machine learning methods presents a novel approach to legal analytics, pushing the boundaries of current predictive capabilities.



Universidad de Oviedo


Machine Learning, Ensemble