randomUniformForest: Random Uniform Forests for Classification, Regression and Unsupervised Learning

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 cut-point 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 three-layer 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

AuthorSaip Ciss
MaintainerSaip Ciss <[email protected]>
LicenseBSD_3_clause + file LICENSE
Package repositoryView on CRAN
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randomUniformForest documentation built on May 29, 2017, 10:18 p.m.