44 lines
1.2 KiB
R
44 lines
1.2 KiB
R
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# tuning random forest over mtry parameter in parallel
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library(foreach)
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library(randomForest)
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# a simple iterator over different values for the mtry argument
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mtryiter <- function(from, to, stepFactor=2) {
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nextEl <- function() {
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if (from > to) stop('StopIteration')
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i <- from
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from <<- ceiling(from * stepFactor)
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i
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}
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obj <- list(nextElem=nextEl)
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class(obj) <- c('abstractiter', 'iter')
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obj
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}
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# vector of ntree values that we're interested in
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vntree <- c(25, 50, 100, 200, 500, 1000)
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# function that gets random forest error information for different values of mtry
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tune <- function(x, y, ntree=vntree, mtry=NULL, keep.forest=FALSE, ...) {
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comb <- if (is.factor(y))
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function(a, b) rbind(a, data.frame(ntree=ntree, mtry=b$mtry, error=b$err.rate[ntree, 1]))
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else
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function(a, b) rbind(a, data.frame(ntree=ntree, mtry=b$mtry, error=b$mse[ntree]))
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foreach(mtry=mtryiter(1, ncol(x)), .combine=comb, .init=NULL,
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.packages='randomForest') %dopar% {
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randomForest(x, y, ntree=max(ntree), mtry=mtry, keep.forest=FALSE, ...)
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}
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}
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# generate the inputs
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x <- matrix(runif(2000), 100)
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y <- gl(2, 50)
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# execute randomForest
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results <- tune(x, y)
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# print the result
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print(results)
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