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

Description Usage Arguments Value Author(s) References See Also Examples

Description

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

Usage

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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


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