  We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a
stable rule learning algorithm which takes the form of a short and simple list
of rules. State-of-the-art learning algorithms are often referred to as "black
boxes" because of the high number of operations involved in their prediction
process. Despite their powerful predictivity, this lack of interpretability may
be highly restrictive for applications with critical decisions at stake. On the
other hand, algorithms with a simple structure-typically decision trees, rule
algorithms, or sparse linear models-are well known for their instability. This
undesirable feature makes the conclusions of the data analysis unreliable and
turns out to be a strong operational limitation. This motivates the design of
SIRUS, which combines a simple structure with a remarkable stable behavior when
data is perturbed. The algorithm is based on random forests, the predictive
accuracy of which is preserved. We demonstrate the efficiency of the method
both empirically (through experiments) and theoretically (with the proof of its
asymptotic stability). Our R/C++ software implementation sirus is available
from CRAN.
