best.first.search.Rd
\name{best.first.search}
\alias{best.first.search}
\title{ Best-first search }
\description{
The algorithm for searching atrribute subset space.
}
\usage{
best.first.search(attributes, eval.fun, max.backtracks = 5)
}
\arguments{
\item{attributes}{ a character vector of all attributes to search in }
\item{eval.fun}{ a function taking as first parameter a character vector of all attributes and returning a numeric indicating how important a given subset is }
\item{max.backtracks}{ an integer indicating a maximum allowed number of backtracks, default is 5 }
}
\details{
The algorithm is similar to \code{\link{forward.search}} besides the fact that is chooses the best node from all already evaluated ones and evaluates it. The selection of the best node is repeated approximately \code{max.backtracks} times in case no better node found.
}
\value{
A character vector of selected attributes.
}
\author{ Piotr Romanski }
\examples{
library(rpart)
data(iris)

evaluator <- function(subset) {
#k-fold cross validation
k <- 5
splits <- runif(nrow(iris))
results = sapply(1:k, function(i) {
test.idx <- (splits >= (i - 1) / k) & (splits < i / k)
train.idx <- !test.idx
test <- iris[test.idx, , drop=FALSE]
train <- iris[train.idx, , drop=FALSE]
tree <- rpart(as.simple.formula(subset, "Species"), train)
error.rate = sum(test\$Species != predict(tree, test, type="c")) / nrow(test)
return(1 - error.rate)
})
print(subset)
print(mean(results))
return(mean(results))
}

subset <- best.first.search(names(iris)[-5], evaluator)
f <- as.simple.formula(subset, "Species")
print(f)

}