View source: R/search.best.first.R
best.first.search | R Documentation |
The algorithm for searching atrribute subset space.
best.first.search(attributes, eval.fun, max.backtracks = 5)
attributes |
a character vector of all attributes to search in |
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 |
max.backtracks |
an integer indicating a maximum allowed number of backtracks, default is 5 |
The algorithm is similar to 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 max.backtracks
times in case no better node found.
A character vector of selected attributes.
Piotr Romanski
forward.search
, backward.search
, hill.climbing.search
, exhaustive.search
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)
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