#ANN으로 iris 분류 작업
idx <- sample(1:nrow(iris),0.7*nrow(iris))
train<-iris[idx, ]
test<-iris[-idx,]
library(nnet)
model_iris1 = nnet(Species ~., train, size=1)
model_iris1

model_iris3 =nnet(Species ~., train, size=3)
model_iris3

summary(model_iris1)
summary(model_iris3)
plot.nnet(summary(model_iris1))
plot.nnet(summary(model_iris3))

#분류 모델 평가
predict(model_iris1, test,type="class")
predict(model_iris3, test,type="class")

#confusion matrix
table(predict(model_iris1,test,type="class"),test$Species)
(13+17+15) / nrow(test) 

table(predict(model_iris3,test,type="class"),test$Species)
(13+16+15) / nrow(test)

#ANN에서 고려 사항
#과적합(Overfitting)
#최적합HiddenLaer찾기