rfProximity

a <code>CORElearn</code> model of type random forest.

model

if <code>TRUE</code>, function returns a proximity matrix, else it returns a distance matrix.

outProximity


Random forest computes similarity between instances with classification of out-of-bag instances. 
If two out-of-bag cases are classified in the same tree leaf the proximity between them is incremented.


robust

cluster

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.

rfProximity: A random forest based proximity function

Description

Usage

Arguments

Value

Details

References

See Also

Examples