rfClustering

 a random forest model returned by <code><a rd-options="" href="/link/CoreModel?package=CORElearn&version=1.48.0" data-mini-rdoc="CORElearn::CoreModel">CoreModel</a></code>

model

noClusters


Creates a clustering of random forest training instances. Random forest provides proximity of its training instances based on their out-of-bag classification.
This information is usually passed to visualizations (e.g., scaling) and attribute importance measures.


robust

cluster

tree

This is a suite of machine learning algorithms written in C++ with R
interface. It contains several machine learning model learning techniques in
classification and regression, for example classification and regression trees with
optional constructive induction and models in the leaves, random forests, kNN,
naive Bayes, and locally weighted regression. All predictions obtained with these
models can be explained and visualized with ExplainPrediction package.
The package is especially strong in feature evaluation where it contains several variants of
Relief algorithm and many impurity based attribute evaluation functions, e.g., Gini,
information gain, MDL, and DKM. These methods can be used for example to discretize
numeric attributes.
Its additional feature is OrdEval algorithm and its visualization used for evaluation
of data sets with ordinal features and class, enabling analysis according to the
Kano model of customer satisfaction.
Several algorithms support parallel multithreaded execution via OpenMP.
The top-level documentation is reachable through ?CORElearn.

Marko Robnik-Sikonja

CORElearn

Classification, Regression and Feature Evaluation

rfClustering function

 a random forest model returned by <code><a rd-options='' href='CoreModel'>CoreModel</a></code>

rfClustering: Random forest based clustering

Description

Usage

Arguments

Value

Details

References

See Also

Examples