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Communication Dans Un Congrès Année : 2018

Rule Induction Partitioning Estimator: Design of an interpretable prediction algorithm

Résumé

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 ∈ X (features). The algorithm extracts a sparse set of hyperrectangles r ⊂ 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 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.
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Dates et versions

hal-03905772 , version 1 (19-12-2022)

Identifiants

Citer

Vincent Margot, Jean-Patrick Baudry, Frédéric Guilloux, Olivier Wintenberger. Rule Induction Partitioning Estimator: Design of an interpretable prediction algorithm. International Conference on Machine Learning and Data Mining in Pattern Recognition, Jul 2018, New York (NY), United States. pp.288-301, ⟨10.1007/978-3-319-96133-0_22⟩. ⟨hal-03905772⟩
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