This study argues that R-squared is a more informative and robust metric for evaluating regression models compared to SMAPE, MAE, MAPE, MSE, and RMSE. Through several synthetic use cases and analysis of two real medical datasets, the authors demonstrate that R-squared provides a more accurate assessment of model performance, particularly when dealing with skewed data or outliers. They propose using R-squared as the standard metric for regression analysis evaluation.

Davide Chicco is an Academic Editor for PeerJ Computer Science.

The paper presents a well-reasoned argument for the superiority of R-squared over other regression metrics, particularly SMAPE. The use cases and examples effectively illustrate the limitations of SMAPE, especially its insensitivity to the distribution of values. The comparison with other commonly used metrics is detailed and insightful. Despite some limitations in the scope of comparison and the lack of a real-world application, the paper's strong methodological approach and clear presentation justify a rating of 4.