Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
School rankings based on value-added (VA) estimates are subject to prediction errors, since VA is defined as the difference between predicted and actual performance. We introduce the use of random forest (RF), rooted in the machine learning literature, as a more flexible approach to minimize prediction errors and to improve school rankings. Monte Carlo simulations demonstrate the advantages of this approach. Applying the proposed method to Italian middle school data indicates that school rankings are sensitive to prediction errors, even when extensive controls are added. RF estimates provide a low-cost way to increase the accuracy of predictions, resulting in more informative rankings, and more impact of policy decisions.