PP.classify {classPP}R Documentation

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

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

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


data(iris)
n <- nrow(iris)
tot <- c(1:n)
n.train <- round(n*0.9)
train <- sample(tot,n.train)
test <- tot[-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[test,1:4],iris[test,5],Tree.result,Rule=1)
tree.test


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