Predict class for the test set and calculate prediction error

Description

After finding tree structure, predict class for the test set and calculate prediction error.

Usage

1
PP.classify(test.data, true.class, Tree.result, Rule, ...) 

Arguments

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)

...

...

Value

A list with components:

predict.class

predicted class

predict.error

prediction error

Author(s)

Eun-kyung Lee

References

Lee E., Cook D., and Klinke, S. (2002) Projection Pursuit indices for supervised classification

See Also

PPindex.class, PP.optimize, PP.Tree

Examples

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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