  Towards a future where machine learning systems will integrate into every
aspect of people's lives, researching methods to interpret such systems is
necessary, instead of focusing exclusively on enhancing their performance.
Enriching the trust between these systems and people will accelerate this
integration process. Many medical and retail banking/finance applications use
state-of-the-art machine learning techniques to predict certain aspects of new
instances. Tree ensembles, like random forests, are widely acceptable solutions
on these tasks, while at the same time they are avoided due to their black-box
uninterpretable nature, creating an unreasonable paradox. In this paper, we
provide a methodology for shedding light on the predictions of the misjudged
family of tree ensemble algorithms. Using classic unsupervised learning
techniques and an enhanced similarity metric, to wander among transparent trees
inside a forest following breadcrumbs, the interpretable essence of tree
ensembles arises. An interpretation provided by these systems using our
approach, which we call "LionForests", can be a simple, comprehensive rule.
