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.
