  RIPE is a novel deterministic and easily understandable prediction algorithm
developed for continuous and discrete ordered data. It infers a model, from a
sample, to predict and to explain a real variable $Y$ given an input variable
$X \in \mathcal X$ (features). The algorithm extracts a sparse set of
hyperrectangles $\mathbf r \subset \mathcal X$, which can be thought of as
rules of the form If-Then. This set is then turned into a partition of the
features space $\mathcal X$ of which each cell is explained as a list of rules
with satisfied their If conditions. The process of RIPE is illustrated on
simulated datasets and its efficiency compared with that of other usual
algorithms.
