Rule Induction Partitioning Estimator
This work addresses the need for interpretable prediction models in data analysis, though it appears incremental as it builds on rule-based methods without claiming broad SOTA improvements.
The authors introduced RIPE, a deterministic and interpretable prediction algorithm for continuous and discrete ordered data, which extracts sparse hyperrectangles as rules to partition the feature space and predict a real variable Y given X, demonstrating its efficiency on simulated datasets compared to other algorithms.
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.