[2603.19291] A Visualization for Comparative Analysis of Regression Models

Computer Science > Machine Learning arXiv:2603.19291 (cs) [Submitted on 10 Mar 2026] Title:A Visualization for Comparative Analysis of Regression Models Authors:Nassime Mountasir (ICube), Baptiste Lafabregue (ICube), Bruno Albert, Nicolas Lachiche (ICube) View a PDF of the paper titled A Visualization for Comparative Analysis of Regression Models, by Nassime Mountasir (ICube) and 3 other authors View PDF Abstract:As regression is a widely studied problem, many methods have been proposed to solve it, each of them often requiring setting different hyper-parameters. Therefore, selecting the proper method for a given application may be very difficult and relies on comparing their performances. Performance is usually measured using various metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or R-squared (R${}^2$). These metrics provide a numerical summary of predictive accuracy by quantifying the difference between predicted and actual values. However, while these metrics are widely used in the literature for summarizing model performance and useful to distinguish between models performing poorly and well, they often aggregate too much information. This article addresses these limitations by introducing a novel visualization approach that highlights key aspects of regression model performance. The proposed method builds upon three main contributions: (1) considering the residuals in a 2D space, which allows for simultaneous evaluation of errors from two m...