一、回歸

二、對回歸準(zhǔn)確性進(jìn)行檢驗(yàn)

代碼：

#加載包

library (tidyverse)#數(shù)據(jù)預(yù)處理

library(tidytext)#分組排序

library(glmnet)# Lasso & Ridgelibrary ( rpart)#決策樹

library(ipred)# Bagging

library( randomForest)#隨機(jī)森林

#、加載數(shù)據(jù)

data( 'mtcars ' )

代碼：

#數(shù)據(jù)預(yù)處理

data = mtcars %>%

mutate(vs = factor(vs , levels = c(0,1), labels = c( 'V-shaped

, 'straight" )) ,

am = factor(am, levels = c(0,1), labels = c ( ' automatic ', 'manual ' )))

x = model.matrix(mpg~. ,data = data)[ ,-1]#啞元變量轉(zhuǎn)換

y = data$mpg

data = data.frame (mpg = y , x)

#數(shù)據(jù)集劃分

set .seed(1)

train_id = sample(1:nrow(data),0.7*nrow(data))train = data[train_id , ]

test = data[-train_id, ]

#訓(xùn)練模型set.seed(1)

linear_reg = lm(mpg~. ,data = train)

stepwise = step(linear_reg,direction = 'both ' ,trace = 0)cv .ridge = cv.glmnet(x = as.matrix (train[ ,-1])，

y = train$mpg,

family = 'gaussian ' ,alpha = 0,

nfolds = 5)

cv.lasso = cv.glmnet(x = as.matrix (train[ ,-1])，

y = train$mpg,

family = 'gaussian ' ,alpha = 1,

nfolds = 5)

tree = rpart(mpg~. ,data = train)

bag_tree = bagging(mpg~. , data = train)

rf = randomForest(mpg~. , data = train , importance = T)

6隨機(jī)森林變量重要性

#變量重要性

varImpPlot(rf ,main = 'Variable Importance in Random Forest' )

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