Regression is a widely studied problem, with numerous methods proposed to solve it, each often requiring the setting of different hyper-parameters. Selecting the proper method for a given application can therefore be complex and relies on comparing their performances.

Innovative Visualization for Model Analysis

This article introduces 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, allowing for simultaneous evaluation of errors from two models.
  2. Leveraging the Mahalanobis distance to account for correlations and differences in scale within the data.
  3. Employing a colormap to visualize the percentile-based distribution of errors, making it easier to identify dense regions and outliers.

Advantages of the Proposed Visualization

By graphically representing the distribution of errors and their correlations, this approach provides a more detailed and comprehensive view of model performance, enabling users to uncover patterns that traditional aggregate metrics may obscure. The proposed visualization method facilitates a deeper understanding of regression model performance differences and error distributions, enhancing the evaluation and comparison process.