Description Usage Arguments Value Author(s) References See Also Examples
After finding tree structure, predict class for the test set and calculate prediction error.
1 | PP.classify(test.data, true.class, Tree.result, Rule, ...)
|
test.data |
the test dataset |
true.class |
true class of test dataset if available |
Tree.result |
the result of PP.Tree |
Rule |
split rule 1 - mean of two group means 2 - weighted mean of two group means 3 - mean of max(left group) and min(right group) 4 - weighted mean of max(left group) and min(right group) |
... |
... |
A list with components:
predict.class |
predicted class |
predict.error |
prediction error |
Eun-kyung Lee
Lee E., Cook D., and Klinke, S. (2002) Projection Pursuit indices for supervised classification
PPindex.class
, PP.optimize
,
PP.Tree
1 2 3 4 5 6 7 8 9 10 11 12 | data(iris)
n <- nrow(iris)
n.train <- round(n*0.9)
train <- sample(n, n.train)
Tree.result <- PP.Tree("LDA", iris[train,5], iris[train, 1:4])
tree.train <- PP.classify(iris[train, 1:4], iris[train, 5], Tree.result,
Rule=1)
tree.train
tree.test <- PP.classify(iris[-train, 1:4], iris[-train, 5],
Tree.result, Rule=1)
tree.test
|
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