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 |
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Author | Saip Ciss |
Maintainer | Saip Ciss <saip.ciss@wanadoo.fr> |
License | BSD_3_clause + file LICENSE |
Version | 1.1.6 |
Package repository | View on CRAN |
Installation |
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