Package: ForestDisc Type: Package Title: Forest Discretization Version: 0.1.0 Author: Haddouchi Maïssae Maintainer: Haddouchi Maïssae Description: Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) , and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) . License: GPL (>= 3) Encoding: UTF-8 Imports: randomForest, nloptr, moments, stats NeedsCompilation: no Packaged: 2026-09-18 10:01:04 UTC; root Config/pak/sysreqs: cmake Repository: https://hmais.r-universe.dev Date/Publication: 2020-03-19 12:00:21 UTC RemoteUrl: https://github.com/cran/ForestDisc RemoteRef: HEAD RemoteSha: ea960566ba7b25142c37c3ae0b444b5dbecc5a71