  The interpretability of random forest (RF) models is a research topic of
growing interest in the machine learning (ML) community. In the state of the
art, RF is considered a powerful learning ensemble given its predictive
performance, flexibility, and ease of use. Furthermore, the inner process of
the RF model is understandable because it uses an intuitive and intelligible
approach for building the RF decision tree ensemble. However, the RF resulting
model is regarded as a "black box" because of its numerous deep decision trees.
Gaining visibility over the entire process that induces the final decisions by
exploring each decision tree is complicated, if not impossible. This complexity
limits the acceptance and implementation of RF models in several fields of
application. Several papers have tackled the interpretation of RF models. This
paper aims to provide an extensive review of methods used in the literature to
interpret RF resulting models. We have analyzed these methods and classified
them based on different axes. Although this review is not exhaustive, it
provides a taxonomy of various techniques that should guide users in choosing
the most appropriate tools for interpreting RF models, depending on the
interpretability aspects sought. It should also be valuable for researchers who
aim to focus their work on the interpretability of RF or ML black boxes in
general.
