从拟合模型推导类的邻域
Deducing neighborhoods of classes from a fitted model
摘要：
当今世界，对庞大数据集的非常复杂模型的需求正在稳步上升。这些模型的问题在于，通过提高模型的复杂性，解释它们变得更加困难。不断发展的\emph{interpretable machine learning}领域试图通过使用有助于更好地理解这些模型的特定技术来弥补这些复杂（甚至是黑盒）模型中缺乏可解释性的问题。本文提出了一种新的可解释的机器学习方法，它可以帮助理解在分类模型中使用分位数移位将特征空间划分为预测类。为了说明在这种情况下，分位数移位方法（QSM）可能会变得有益，它被应用到一个理论医学的例子和一个真实的数据的例子。基本上，使用真实的数据点（或特定的兴趣点），并观察在略微提高或降低特定特征后预测的变化。通过比较操作之前和之后的预测，在某些条件下，观察到的预测变化可以被解释为关于被操作特征的类的邻域。弦线图用于可视化观察到的变化。
英文摘要：
In todays world the request for very complex models for huge data sets is rising steadily. The problem with these models is that by raising the complexity of the models, it gets much harder to interpret them. The growing field of \emph{interpretable machine learning} tries to make up for the lack of interpretability in these complex (or even blackbox-)models by using specific techniques that can help to understand those models better. In this article a new kind of interpretable machine learning method is presented, which can help to understand the partitioning of the feature space into predicted classes in a classification model using quantile shifts. To illustrate in which situations this quantile shift method (QSM) could become beneficial, it is applied to a theoretical medical example and a real data example. Basically, real data points (or specific points of interest) are used and the changes of the prediction after slightly raising or decreasing specific features are observed. By comparing the predictions before and after the manipulations, under certain conditions the observed changes in the predictions can be interpreted as neighborhoods of the classes with regard to the manipulated features. Chordgraphs are used to visualize the observed changes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
