PP.classify {classPP} | R Documentation |
After finding tree structure, predict class for the test set and calculate prediction error.
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) |
... |
... |
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}
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