breastCancer

<p>Original Wisconsin Breast Cancer Database</p>

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.

Saip Ciss

randomUniformForest

Random Uniform Forests for Classification, Regression and
Unsupervised Learning

breastCancer function

<p>A matrix containing 699 observations and 10 attributes with missing values.</p>

breastCancer: Breast Cancer Wisconsin (Original) Data Set

Description

Usage

Arguments

Format

References