Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests.

wineQualityRed

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.

wineQualityRed: Wine Quality Data Set

Description

Usage

Arguments

format

source

References