Multinomial classification using neuralnet package

Question

This question ought to be real simple. But the documentation isn't helping.

I am using R. I must use the neuralnet package for a multinomial classification problem.

All examples are for binomial or linear output. I could do some one-vs-all implementation using binomial output. But I believe I should be able to do this by having 3 units as the output layer, where each is a binomial (ie. probability of that being the correct output). No?

This is what I would using nnet (which I believe is doing what I want):

data(iris)
library(nnet)
m1 <- nnet(Species ~ ., iris, size = 3)
table(predict(m1, iris, type = "class"), iris$Species)

This is what I am trying to do using neuralnet (the formula hack is because neuralnet does not seem to support the '.' notation in the formula):

data(iris)
library(neuralnet)
formula <- paste('Species ~', paste(names(iris)[-length(iris)], collapse='+'))
m2 <- neuralnet(formula, iris, hidden=3, linear.output=FALSE)
# fails !

Answer 1

You are right that the formula interface of neuralnet() does not support '.'.

However, the problem with your code above is rather that a factor is not accepted as target. You have to expand the factor Species to three binary variables first. Ironically, this works best with the function class.ind() from the nnet package (which wouldn't need such a function, since nnet() and multinom() work fine with factors):

trainData <- cbind(iris[, 1:4], class.ind(iris$Species))
neuralnet(setosa + versicolor + virginica ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, trainData)

This works - at least for me.