Iris - Neural Network

Loading Package and treating the data

data(iris)
iris$setosa <- iris$Species=="setosa"
iris$virginica <- iris$Species == "virginica"
iris$versicolor <- iris$Species == "versicolor"
iris.train.idx <- sample(x = nrow(iris), size = nrow(iris)*0.5)
iris.train <- iris[iris.train.idx,]
iris.valid <- iris[-iris.train.idx,]

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:    1058    error: 0.00016  time: 0.41 secs
## hidden: 10, 10    thresh: 0.001    rep: 2/5    steps:     481    error: 0.00015  time: 0.27 secs
## hidden: 10, 10    thresh: 0.001    rep: 3/5    steps:     912    error: 0.00012  time: 0.31 secs
## hidden: 10, 10    thresh: 0.001    rep: 4/5    steps:     668    error: 0.00025  time: 0.24 secs
## hidden: 10, 10    thresh: 0.001    rep: 5/5    steps:    1017    error: 0.00016  time: 0.39 secs

plot(iris.net, rep="best")

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         26          0         0
##   versicolor      0         22         1
##   virginica       0          1        25