VSURF : un package R pour la sélection de variables à l'aide de forêts aléatoires
Year: 2014
Type: conference-paper
Abstract: This paper describes the R package VSURF. Based on random forests, it delivers two subsets of variables according to two types of variable selection for clas-sification or regression problems. The first is a subset of important variables which are relevant for interpretation, while the second one is a subset corresponding to a parsimo-nious prediction model. The strategy is based on a preliminary ranking of the explanatory variables using the random forests permutation-based score of importance and proceeds using a stepwise ascending variable introduction strategy. The two proposals can be ob-tained automatically using data-driven default values, good enough to provide interesting results, but can also be fine-tuned by the user. The algorithm is illustrated on a simulated example and its applications to real datasets are presented.
Source: HAL
Language: French
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2301386953