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Rule Induction Partitioning Estimator

2018; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-319-96133-0_22

1611-3349

Vincent Margot, Jean-Patrick Baudry, Frédéric Guilloux, Olivier Wintenberger,

Statistical Methods and Inference

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.