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In the context of deploying robust solutions in production environments, evaluating the performance of different machine learning models on a specific application is crucial. While common metrics like the mean square error offer a concise summary of model accuracy, they may overlook nuanced differences in prediction performance. In this paper, we propose a visual comparison method based on analyzing the distribution of residuals across multiple machine learning models. By sorting, visualizing, and defining a cutoff value, we aim to provide machine learning engineers with a comprehensive yet practical approach to assess model performance, enabling informed decision-making for real-world applications. The corresponding code is published on GitHub.

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Comparative Analysis of Machine Learning Models in Production Environments Through Residual Distributions

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Jan A. Zak,
  • Christian Weißenfels

摘要

In the context of deploying robust solutions in production environments, evaluating the performance of different machine learning models on a specific application is crucial. While common metrics like the mean square error offer a concise summary of model accuracy, they may overlook nuanced differences in prediction performance. In this paper, we propose a visual comparison method based on analyzing the distribution of residuals across multiple machine learning models. By sorting, visualizing, and defining a cutoff value, we aim to provide machine learning engineers with a comprehensive yet practical approach to assess model performance, enabling informed decision-making for real-world applications. The corresponding code is published on GitHub.