A Visualization for Comparative Analysis of Regression Models
Nassime Mountasir (ICube), Baptiste Lafabregue (ICube), Bruno Albert, Nicolas Lachiche (ICube)

TL;DR
This paper introduces a novel visualization technique for comparing regression models by representing residuals in a 2D space, using Mahalanobis distance and colormaps to reveal detailed error patterns beyond traditional metrics.
Contribution
It presents a new visualization approach that enhances regression model comparison by considering residual correlations and error distributions more comprehensively.
Findings
Enables simultaneous evaluation of two models' errors.
Highlights dense regions and outliers in residual distributions.
Provides deeper insights into model performance differences.
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). 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…
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Taxonomy
TopicsData Analysis with R
