The method evaluates the quality of the features/attributes/dependent variables
used in the given random forest
The model of type
Training instances that produced random forest
A clustering vector of
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.
rfAttrEvalClustering uses a clustering of the training instances to produce
importance score of attributes
for each cluster separately. If parameter
clustering is set to
the actual class values of the instances are used as clusters thereby producing the evaluation of attributes
specific for each of the class values.
In case of
rfAttrEval a vector of evaluations for the features in the order specified by the formula used to generate the provided
In case of
rfAttrEvalClustering a matrix is returned, where each row contains evaluations for one of the clusters.
Marko Robnik-Sikonja (thesis supervisor) and John Adeyanju Alao (as a part of his BSc thesis)
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
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# build random forests model with certain parameters modelRF <- CoreModel(Species ~ ., iris, model="rf", selectionEstimator="MDL", minNodeWeightRF=5, rfNoTrees=100, maxThreads=1) rfAttrEval(modelRF) # feature evaluations x <- rfAttrEval(modelRF) # feature evaluations for each class print(x) destroyModels(modelRF) # clean up
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