carEvaluation

<p>Derived from simple hierarchical decision model, this database may be useful for testing constructive induction and structure discovery methods.</p>

CarEvaluation

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

CarEvaluation function

<p>A matrix containing 1728 observations and 6 attributes.</p>

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CarEvaluation: Car Evaluation Data Set

Description

Usage

Arguments

Format