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This paper introduces Fast Calibrated Explanations, an extension of an existing explanation method, Calibrated Explanations, designed for generating rapid, uncertainty-aware explanations for machine learning models. By incorporating perturbation techniques from ConformaSight, a global explanation method, into the core elements of Calibrated Explanations, we achieved significant speedups. These core elements include local feature importance with calibrated predictions, both of which retain uncertainty quantification. While the extension sacrifices some degree of detail, it excels in computational efficiency, making it ideal for high-stakes, real-time applications. Fast Calibrated Explanations applies to probabilistic explanations in classification and thresholded regression tasks, providing the probability of a target being above or below a user-defined threshold. This approach maintains the versatility of Calibrated Explanations for both classification and thresholded regression, making it suitable for a range of predictive tasks where uncertainty quantification is crucial.

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Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Tuwe Löfström,
  • Fatima Rabia Yapicioglu,
  • Alessandra Stramiglio,
  • Helena Löfström,
  • Fabio Vitali

摘要

This paper introduces Fast Calibrated Explanations, an extension of an existing explanation method, Calibrated Explanations, designed for generating rapid, uncertainty-aware explanations for machine learning models. By incorporating perturbation techniques from ConformaSight, a global explanation method, into the core elements of Calibrated Explanations, we achieved significant speedups. These core elements include local feature importance with calibrated predictions, both of which retain uncertainty quantification. While the extension sacrifices some degree of detail, it excels in computational efficiency, making it ideal for high-stakes, real-time applications. Fast Calibrated Explanations applies to probabilistic explanations in classification and thresholded regression tasks, providing the probability of a target being above or below a user-defined threshold. This approach maintains the versatility of Calibrated Explanations for both classification and thresholded regression, making it suitable for a range of predictive tasks where uncertainty quantification is crucial.