CORElearn (version 0.9.29)

rfAttrEval: Attribute evaluation with random forest

Description

The method evaluates the quality of the features/attributes/dependent variables used in the given random forest model.

Usage

rfAttrEval(model)

Arguments

model

The model of type rf or rfNear as returned by CoreModel.

Value

• Vector of evaluations for the features in the order specified by the formula used to generate provided model.

Details

The attributes are evaluated via provided random forest's out-of-bag sets. Values for each attribute in turn are randomly shuffled and classified with random forest. The difference between average margin of non-shuffled and shuffled instances serves as a quality estimate of the attribute.

References

Marko Robnik-Sikonja: Improving Random Forests. In J.-F. Boulicaut et al.(Eds): ECML 2004, LNAI 3210, Springer, Berlin, 2004, pp. 359-370 Available also from http://lkm.fri.uni-lj.si/rmarko/papers/ Leo Breiman: Random Forests. Machine Learning Journal, 2001, 45, 5-32

See Also

CORElearn, CoreModel. attrEval.

Examples

# use iris data set

# build random forests model with certain parameters
modelRF <- CoreModel(Species ~ ., iris, model="rf", 
              selectionEstimator="MDL",minNodeWeight=5,rfNoTrees=100)

estRF <- rfAttrEval(modelRF) 

print(estRF)