RMSE vs MAE Performance Comparison Across ML Models and Teams #1393891 (License: Personal Use)

This grouped bar chart displays performance metrics-Root Mean Square Error (RMSE, blue) and Mean Absolute Error (MAE, red)-for six machine learning entries: Baseline, Brunel-BeiJiang, Cubic-ASU, MIT-Lincoln, team-australia, and Uni-Ulm. Cubic-ASU exhibits the highest RMSE (14.2) but a relatively strong MAE (11.3), while MIT-Lincoln achieves the lowest MAE (6.5) despite moderate RMSE (8.6). The chart highlights trade-offs between error sensitivity and robustness across models.

Used in technical reports, AI competition summaries, or model evaluation pages to illustrate comparative regression performance; targets data scientists, ML engineers, and researchers evaluating model accuracy.