新工具包提升分类模型在数据分布变化时的置信度准确性
Protected Probabilistic Classification Library
- 基于后处理校准技术，适配训练与测试数据分布偏移
- 在二分类和多分类任务中表现优于现有方法
- 适合批量及在线学习场景中的可靠性评估
本文介绍了一个新的Python工具包，专门用于解决数据集偏移下概率分类器的校准问题。该方法在二分类和多分类设置中进行了验证，并与多种现有后处理校准方法进行了对比。实验结果表明，该技术在训练与测试数据分布发生变化的场景下，能有效提升分类器置信度的准确性，适用于批量学习和在线学习中的各类分类任务。
原文摘要 · Abstract (English)
This paper introduces a new Python package specifically designed to address calibration of probabilistic classifiers under dataset shift. The method is demonstrated in binary and multi-class settings and its effectiveness is measured against a number of existing post-hoc calibration methods. The empirical results are promising and suggest that our technique can be helpful in a variety of settings for batch and online learning classification problems where the underlying data distribution changes between the training and test sets.
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