The need for interpreting machine learning models is addressed through prototype explanations within the context of tree ensembles. An algorithm named Adaptive Prototype Explanations of Tree Ensembles (A-PETE) is proposed to automatise the selection of prototypes for these classifiers. Its unique characteristics is using a specialised distance measure and a modified k-medoid approach. Experiments demonstrated its competitive predictive accuracy with respect to earlier explanation algorithms. It also provides a a sufficient number of prototypes for the purpose of interpreting the random forest classifier.

翻译：在树集成模型的背景下，通过原型解释来满足对机器学习模型可解释性的需求。本文提出了一种名为自适应原型解释树集成（A-PETE）的算法，用于自动化地为这类分类器选择原型。其独特之处在于采用了专门的距离度量方法和改进的k-medoid方法。实验表明，该算法在预测准确性方面与先前的解释算法相比具有竞争力。同时，它能为随机森林分类器的解释提供足够数量的原型。