Ensemble model, for classification and regression, based on a forest of unpruned and randomized binary 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 Uniform distribution on the support of each candidate variable. Optimal random node is, then, selected by maximizing information gain (classification) or minimizing 'L2' (or 'L1') distance (regression). Data are either bootstrapped or sub-sampled for each tree. Random Uniform Forests are aimed to lower correlation between trees, to offer more details about variable importance and selection and to allow native incremental learning.
methods, Rcpp (>= 0.11.1), parallel, doParallel, iterators,
foreach (>= 1.4.2), ggplot2, pROC, gtools

Random Uniform Forests for Classification, Regression and
Unsupervised Learning
Ensemble model, for classification, regression and unsupervised learning, base on a forest of unpruned and randomized binary decision trees.
methods, Rcpp (>= 0.11.1), parallel, doParallel, iterators,
foreach (>= 1.4.2), ggplot2, pROC, gtools, cluster, MASS

Ensemble model, for classification, regression and unsupervised learning, based on a forest of unpruned and randomized binary decision trees.

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.

methods, Rcpp (>= 0.11.1), parallel, doParallel, iterators,
foreach (>= 1.4.2), ggplot2, pROC, cluster, MASS