  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.
