测量用户模型的预测性能:细节很重要

Radek Pelánek
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引用次数: 5

摘要

用户建模技术的评估通常基于模型的预测准确性。预测准确性的量化是使用性能度量来完成的。我们展示了性能度量的选择是重要的,甚至度量计算的细节也很重要。我们详细分析了学生建模环境中两种常用的度量(AUC, RMSE)。我们讨论了不同的计算方法(全局、跨技能平均、跨学生平均),并表明这些方法具有不同的性质。对近年来的研究论文的分析表明,对度量计算的描述往往不够充分。为了使研究结论有效和可重复,研究人员需要更加注意性能指标的选择,并且需要更明确地描述其计算的细节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring Predictive Performance of User Models: The Details Matter
Evaluation of user modeling techniques is often based on the predictive accuracy of models. The quantification of predictive accuracy is done using performance metrics. We show that the choice of a performance metric is important and that even details of metric computation matter. We analyze in detail two commonly used metrics (AUC, RMSE) in the context of student modeling. We discuss different approaches to their computation (global, averaging across skill, averaging across students) and show that these methods have different properties. An analysis of recent research papers shows that the reported descriptions of metric computation are often insufficient. To make research conclusions valid and reproducible, researchers need to pay more attention to the choice of performance metrics and they need to describe more explicitly details of their computation.
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