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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