[R] accuracy of a neural net
onyourmark
william108 at gmail.com
Mon May 25 14:53:40 CEST 2009
It looks promising. I saw the pdf
at:http://cran.r-project.org/web/packages/caret/vignettes/caretMisc.pdf.
I will give it a try.
Thank you.
onyourmark wrote:
>
> Hi. I started with a file which was a sparse 982x923 matrix and where the
> last column was a variable to be predicted. I did principle component
> analysis on it and arrived at a new 982x923 matrix.
> Then I ran the code below to get a neural network using nnet and then
> wanted to get a confusion matrix or at least know how accurate the neural
> net was. I used the first 22 principle components only for the inputs for
> the neural net.
> I got a perfect prediction rate which is somewhat suspect ( I was using
> the same data for training and prediction but I did not expect perfect
> prediction anyway). So I tried using only a sample of records to build the
> neural net.
> Even with this sample I got 980 out of 982 correct. Can anyone spot an
> error here?
>
> crs$dataset <- read.csv("file:///C:/dataForR/textsTweet1/cleanForPC.csv",
> na.strings=c(".", "NA", "", "?"))
> crs$nnet <- nnet(Value ~ ., data=crs$dataset[,c(1:22,922)], size=10,
> linout=TRUE, skip=TRUE, trace=FALSE, maxit=1000)
>
> targets=crs$dataset[,922]
>
> rawpredictions =predict(crs$nnet, crs$dataset[, c(1:22)], type="raw")
>
> roundedpredictions=round(rawpredictions[,1],digits = 0)
>
> trueAndPredicted=cbind(roundedpredictions, targets)
>
> howManyEqual=trueAndPredicted[,1]==trueAndPredicted[,2]
> sum(howManyEqual)
>
>
> samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
> samp <- c(sample(1:250,125), sample(251:500,125), sample(500:920,300))
>
> crs$nnet <- nnet(Value ~ ., data=crs$dataset[samp,c(1:22,922)], size=10,
> linout=TRUE, skip=TRUE, trace=FALSE, maxit=1000)
>
>
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