随机森林

 library(naniar) 

data("iris")

iris <- iris[,-c(5)]

any_na(iris)

library(randomForest)

model <- randomForest(Sepal.Length ~ Sepal.Width+Petal.Length+Petal.Width, data = iris,   

                      importane = T, proximity = T, 

                      do.trace = 100,na.action = na.roughfix)

par(mfrow=c(1,1))

plot(model) 

importance(model)

library(rminer)

rf <- Importance(model,iris,measure="AAD")

rf$imp

par(mfrow=c(1,3))

boxplot(iris$Sepal.Width)$out

boxplot(iris$Petal.Length)$out

boxplot(iris$Petal.Width)$out

#因為離群值不多，所以先不轉

n <- nrow(iris)

set.seed(1117)

subiris <- sample(seq_len(n), size = round(0.7 * n))

traindata <- iris[subiris,]

testdata <- iris[ - subiris,]

features <- setdiff(x = names(traindata), y = "Sepal.Length")

par(mfrow=c(1,1))

set.seed(123)

tuneRF(x = traindata[features], y = traindata$Sepal.Length,

       mtryStart = 1,ntreeTry = 450)

set.seed(123)

model <- randomForest(Sepal.Length ~ Sepal.Width+Petal.Length+Petal.Width, data = traindata,    

                      ntree = 450, mtry = 3,

                      do.trace = 50,na.action = na.roughfix)

future <- predict(model,testdata)

future <- as.data.frame(future)

final <- cbind(future,testdata)

library(dplyr)

final <- mutate(final,mape=abs(future-Sepal.Length)/Sepal.Length)

mean(final$mape)