  In this paper, we present a novel method to compute decision rules to build a
more accurate interpretable machine learning model, denoted as ExMo. The ExMo
interpretable machine learning model consists of a list of IF...THEN...
statements with a decision rule in the condition. This way, ExMo naturally
provides an explanation for a prediction using the decision rule that was
triggered. ExMo uses a new approach to extract decision rules from the training
data using term frequency-inverse document frequency (TF-IDF) features. With
TF-IDF, decision rules with feature values that are more relevant to each class
are extracted. Hence, the decision rules obtained by ExMo can distinguish the
positive and negative classes better than the decision rules used in the
existing Bayesian Rule List (BRL) algorithm, obtained using the frequent
pattern mining approach. The paper also shows that ExMo learns a qualitatively
better model than BRL. Furthermore, ExMo demonstrates that the textual
explanation can be provided in a human-friendly way so that the explanation can
be easily understood by non-expert users. We validate ExMo on several datasets
with different sizes to evaluate its efficacy. Experimental validation on a
real-world fraud detection application shows that ExMo is 20% more accurate
than BRL and that it achieves accuracy similar to those of deep learning
models.
