Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest
  models
Haddouchi Maissae Berrado Abdelaziz
Random Forest (RF) is well-known as an efficient ensemble learning method in
terms of predictive performance. It is also considered a Black Box because of
its hundreds of deep decision trees. This lack of interpretability can be a
real drawback for acceptance of RF models in several real-world applications,
especially those affecting one's lives, such as in healthcare, security, and
law. In this work, we present Forest-ORE, a method that makes RF interpretable
via an optimized rule ensemble (ORE) for local and global interpretation.
Unlike other rule-based approaches aiming at interpreting the RF model, this
method simultaneously considers several parameters that influence the choice of
an interpretable rule ensemble. Existing methods often prioritize predictive
performance over interpretability coverage and do not provide information about
existing overlaps or interactions between rules. Forest-ORE uses a
mixed-integer optimization program to build an ORE that considers the trade-off
between predictive performance, interpretability coverage, and model size (size
of the rule ensemble, rule lengths, and rule overlaps). In addition to
providing an ORE competitive in predictive performance with RF, this method
enriches the ORE through other rules that afford complementary information. It
also enables monitoring of the rule selection process and delivers various
metrics that can be used to generate a graphical representation of the final
model. This framework is illustrated through an example, and its robustness is
assessed through 36 benchmark datasets. A comparative analysis of well-known
methods shows that Forest-ORE provides an excellent trade-off between
predictive performance, interpretability coverage, and model size.