PPclassify: Predict class for the test set and calculate prediction error...

In natydasilva/PPforest2: Projection Pursuit Classification Forest

Predict class for the test set and calculate prediction error after finding the PPtree structure, .

Description

Predict class for the test set and calculate prediction error after finding the PPtree structure, .

Usage

PPclassify( Tree.result, test.data = NULL, Rule = 1, true.class = NULL)

Arguments

Tree.result	the result of PP.Tree
test.data	the test dataset
Rule	split rule 1:mean of two group means, 2:weighted mean, 3: mean of max(left group) and min(right group), 4: weighted mean of max(left group) and min(right group)
true.class	true class of test dataset if available

Value

predict.class predicted class

predict.error prediction error

References

Lee, YD, Cook, D., Park JW, and Lee, EK(2013) PPtree: Projection pursuit classification tree, Electronic Journal of Statistics, 7:1369-1386.

Examples

#crab data set
set.seed(143)
idx <-sample(1:200, 150)
Tree.crab <- PPtree_split('Type~.', data = crab[idx,], PPmethod = 'LDA', size.p = 1)
Tree.crab
PPclassify(Tree.crab, test.data = crab[-idx, 2:6], Rule = 1,true.class = crab[-idx, 1])

natydasilva/PPforest2 documentation built on July 3, 2025, 8:58 p.m.