This paper describes methods for comparative evaluation of the
interpretability of models of high dimensional time series data inferred by
unsupervised machine learning algorithms. The time series data used in this
investigation were logs from an immersive simulation like those commonly used
in education and healthcare training. The structures learnt by the models
provide representations of participants' activities in the simulation which are
intended to be meaningful to people's interpretation. To choose the model that
induces the best representation, we designed two interpretability tests, each
of which evaluates the extent to which a model's output aligns with people's
expectations or intuitions of what has occurred in the simulation. We compared
the performance of the models on these interpretability tests to their
performance on statistical information criteria. We show that the models that
optimize interpretability quality differ from those that optimize (statistical)
information theoretic criteria. Furthermore, we found that a model using a
fully Bayesian approach performed well on both the statistical and
human-interpretability measures. The Bayesian approach is a good candidate for
fully automated model selection, i.e., when direct empirical investigations of
interpretability are costly or infeasible.

本文介绍了比较评估高维时间序列数据模型可解释性的方法，使用了常用的教育和医疗培训模拟中的日志数据，设计了两种解释性测试，评估模型输出与人们对模拟发生的期望或直觉的一致程度，发现完全贝叶斯方法在统计和人类可解释性测试的性能方面表现良好，是完全自动化模型选择的一个良好的选择。