Network Aplication

library(neuralnet)
iris.net <- neuralnet(setosa+versicolor+virginica ~ 
                      Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, 
                      data=iris.train, hidden=c(10,10), rep = 5, err.fct = "ce", 
                      linear.output = F, lifesign = "minimal", stepmax = 1000000,
                      threshold = 0.001)

hidden: 10, 10    thresh: 0.001    rep: 1/5    steps:    1315   error: 0.00014  time: 0.28 secs
hidden: 10, 10    thresh: 0.001    rep: 2/5    steps:     631   error: 0.00014  time: 0.1 secs
hidden: 10, 10    thresh: 0.001    rep: 3/5    steps:    2748   error: 6e-05    time: 0.46 secs
hidden: 10, 10    thresh: 0.001    rep: 4/5    steps:     665   error: 0.00011  time: 0.12 secs
hidden: 10, 10    thresh: 0.001    rep: 5/5    steps:     820   error: 0.00011  time: 0.15 secs

Predicting Result

iris.prediction <- compute(iris.net, iris.valid[-5:-8])
idx <- apply(iris.prediction$net.result, 1, which.max)
predicted <- c('setosa', 'versicolor', 'virginica')[idx]
table(predicted, iris.valid$Species)

            
predicted    setosa versicolor virginica
  setosa         29          0         0
  versicolor      0         25         5
  virginica       0          1        15
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