(This article was originally published at R snippets, and syndicated at StatsBlogs.)
Although the book is in Polish, sources of all procedures used in it, which are available on my website, can be used without the book. Here is a simplified code from exercise 4.5 presenting neural network bagging:
library(nnet)
set.seed(1)
SAMPLE_SIZE <- 256
X <- seq(-2, 2, length.out = SAMPLE_SIZE)
TRUE_Y <- X ^ 2 / 2 + sin(4 * X)
y <- TRUE_Y + 2 * rnorm(SAMPLE_SIZE)
GetBootstrapPrediction <- function() {
bootstrap.indices <- sample(SAMPLE_SIZE, replace = T)
bootstrap.sample.y <- y[bootstrap.indices]
bootstrap.sample.x <- X[bootstrap.indices]
bootstrap.model <- nnet(bootstrap.sample.y ~bootstrap.sample.x,
lin = T, size = 4, trace = FALSE, maxit = 10 ^ 6)
return(predict(bootstrap.model, data.frame(bootstrap.sample.x = X)))
}
progress.bar <- winProgressBar("Progress in %", "0% done", 0, 1, 0)
BOOTSTRAP_REPLICATIONS <- 1024
bootstrap.predictions <- rep(0, SAMPLE_SIZE)
for (i in 1:BOOTSTRAP_REPLICATIONS) {
bootstrap.predictions <-bootstrap.predictions +
GetBootstrapPrediction()
percentage <- i / BOOTSTRAP_REPLICATIONS
setWinProgressBar(progress.bar, percentage, "Progress in %",
sprintf("%d%% done", round(100 * percentage)))
}
close(progress.bar)
plot(X, y,xlim = c(-2, 2), ylim = c(-5, 6))
lines(X, TRUE_Y, lwd = 4)
lines(X, bootstrap.predictions /BOOTSTRAP_REPLICATIONS,lwd = 3, col = 3)
It produces the following graph. Circles represent training data, black line is true relationship and green line is prediction from bagging procedure:
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