Classification using neural net in r

October 9, 2013

(This article was first published on Analytics , Education , Campus and beyond, and kindly contributed to R-bloggers)

This is mostly for my students and myself for future reference.

Classification is a supervised task , where we need preclassified data and then on new data , I can predict.
Generally we holdout a % from the data available for testing and we call them training and testing data respectively.  So it’s like this , if we know which emails are spam , then only using classification we can predict the emails as spam.

I used the dataset .  The data set has 7 real valued attributes and 1 for predicting . has influenced many of the writing , probably I am making it more obvious.

The library to be used is library(nnet) , below are the list of commands for your reference

1.       Read from dataset
2.       Setting training set index ,  210 is the dataset size, 147 is 70 % of that
   seedstrain<- sample(1:210,147)
3.       Setting test set index
   seedstest <- setdiff(1:210,seedstrain)
4.       Normalize the value to be predicted , use that attribute of the dataset , that you want to predict
   ideal <- class.ind(seeds$Class)
5.       Train the model, -8 because you want to leave out the class attribute , the dataset had a total of 8 attributes with the last one as the predicted one
   seedsANN = nnet(irisdata[seedstrain,-8], ideal[seedstrain,], size=10, softmax=TRUE)
6.       Predict on testset
   predict(seedsANN, seeds[seedstrain,-8], type="class")
7.       Calculate Classification accuracy

   table(predict(seedsANN, seeds[seedstest,-8], type="class"),seeds[seedstest,]$Class)

Happy Coding !

To leave a comment for the author, please follow the link and comment on their blog: Analytics , Education , Campus and beyond. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.

Search R-bloggers


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)