View source: R/search.greedy.R
greedy.search | R Documentation |
The algorithms for searching atrribute subset space.
backward.search(attributes, eval.fun)
forward.search(attributes, eval.fun)
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 |
These algorithms implement greedy search. At first, the algorithms expand starting node, evaluate its children and choose the best one which becomes a new starting node. This process goes only in one direction. forward.search
starts from an empty and backward.search
from a full set of attributes.
A character vector of selected attributes.
Piotr Romanski
best.first.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 <- forward.search(names(iris)[-5], evaluator)
f <- as.simple.formula(subset, "Species")
print(f)
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