Original Wisconsin Breast Cancer Database

breastCancer

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

A matrix containing 699 observations and 10 attributes with missing values.

format

http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Original)

breastCancer: Breast Cancer Wisconsin (Original) Data Set

Description

Usage

Arguments

format

source

References