library(MASS)
library(randomForest)
library(caret)
set.seed(10)
## 실험용 데이어 준비
test = rbind(iris[21:65,],iris[91:110,])
training = rbind(rbind(iris[1:20,],iris[66:90,]),iris[111:150,])
## 학습
rf.fit = randomForest(Species ~ ., data= training, mtry = floor(sqrt(ncol(iris))), ntree = 500, importance = T)
## supervisory로 하려면
rf.fit = randomForest(Species ~ ., data= iris, mtry = max(1,floor(ncol(iris)/3)), ntree = 500, importance = T)
rf.fit
## 성능 평가
y_pred = predict(rf.fit, test)
y_pred
confusionMatrix(y_pred, test$Species)
