Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees. Each tree is grown by sampling, with replacement, a set of variables at each node. Each cutpoint is generated randomly, according to the continuous Uniform distribution. For each tree, data are either bootstrapped or subsampled. The unsupervised mode introduces clustering, dimension reduction and variable importance, using a threelayer engine. Random Uniform Forests are mainly aimed to lower correlation between trees (or trees residuals), to provide a deep analysis of variable importance and to allow native distributed and incremental learning.
Package details 


Author  Saip Ciss 
Date of publication  20150216 21:29:00 
Maintainer  Saip Ciss <saip.ciss@wanadoo.fr> 
License  BSD_3_clause + file LICENSE 
Version  1.1.5 
Package repository  View on CRAN 
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