r语言airquality 岭回归、lasso回归、决策树回归效果检验R语言代码
时间: 2023-12-08 14:03:15 浏览: 59
岭回归和lasso回归可以使用glmnet包,决策树回归可以使用rpart包来实现。下面是一个简单的示例代码,使用airquality数据集进行回归分析:
岭回归:
```R
library(glmnet)
data(airquality)
# 岭回归模型
x <- model.matrix(Ozone ~ ., data = airquality)[,-1]
y <- airquality$Ozone
ridge_mod <- glmnet(x, y, alpha = 0, lambda = 0.1)
plot(ridge_mod)
```
lasso回归:
```R
library(glmnet)
data(airquality)
# lasso回归模型
x <- model.matrix(Ozone ~ ., data = airquality)[,-1]
y <- airquality$Ozone
lasso_mod <- glmnet(x, y, alpha = 1, lambda = 0.1)
plot(lasso_mod)
```
决策树回归:
```R
library(rpart)
data(airquality)
# 决策树回归模型
fit <- rpart(Ozone ~ ., data = airquality, method = "anova")
summary(fit)
```
在实际应用中,需要对模型进行交叉验证,并计算模型的预测准确率等指标来评估模型的效果。
相关问题
r语言airquality 岭回归、lasso回归、决策树回归模型准确率R语言代码
以下是针对airquality数据集进行岭回归、lasso回归和决策树回归模型的R语言代码:
1. 岭回归模型:
```R
# 加载必要的包
library(glmnet)
# 加载数据集
data(airquality)
# 将缺失值替换为平均值
airquality$Ozone[is.na(airquality$Ozone)] <- mean(airquality$Ozone, na.rm = TRUE)
# 将数据集分为训练集和测试集
set.seed(123)
train <- sample(1:nrow(airquality), nrow(airquality)*0.7)
train.airquality <- airquality[train,]
test.airquality <- airquality[-train,]
# 岭回归模型
ridge.model <- glmnet(x = model.matrix(Ozone ~ ., data = train.airquality)[,-1],
y = train.airquality$Ozone,
alpha = 0,
lambda = seq(0,1,0.01))
# 预测
ridge.pred <- predict(ridge.model, newx = model.matrix(Ozone ~ ., data = test.airquality)[,-1])
# 计算均方误差
mean((test.airquality$Ozone - ridge.pred)^2)
```
2. Lasso回归模型:
```R
# 加载必要的包
library(glmnet)
# 加载数据集
data(airquality)
# 将缺失值替换为平均值
airquality$Ozone[is.na(airquality$Ozone)] <- mean(airquality$Ozone, na.rm = TRUE)
# 将数据集分为训练集和测试集
set.seed(123)
train <- sample(1:nrow(airquality), nrow(airquality)*0.7)
train.airquality <- airquality[train,]
test.airquality <- airquality[-train,]
# Lasso回归模型
lasso.model <- glmnet(x = model.matrix(Ozone ~ ., data = train.airquality)[,-1],
y = train.airquality$Ozone,
alpha = 1,
lambda = seq(0,1,0.01))
# 预测
lasso.pred <- predict(lasso.model, newx = model.matrix(Ozone ~ ., data = test.airquality)[,-1])
# 计算均方误差
mean((test.airquality$Ozone - lasso.pred)^2)
```
3. 决策树回归模型:
```R
# 加载必要的包
library(rpart)
# 加载数据集
data(airquality)
# 将缺失值替换为平均值
airquality$Ozone[is.na(airquality$Ozone)] <- mean(airquality$Ozone, na.rm = TRUE)
# 将数据集分为训练集和测试集
set.seed(123)
train <- sample(1:nrow(airquality), nrow(airquality)*0.7)
train.airquality <- airquality[train,]
test.airquality <- airquality[-train,]
# 决策树回归模型
tree.model <- rpart(Ozone ~ ., data = train.airquality)
# 预测
tree.pred <- predict(tree.model, newdata = test.airquality)
# 计算均方误差
mean((test.airquality$Ozone - tree.pred)^2)
```
R语言airquality
`airquality` 是一个 R 语言中自带的数据集,它记录了纽约市在 1973 年 5 月至 9 月期间每天的空气质量指标。这个数据集包含了五个变量:
- Ozone:臭氧浓度(ppb)
- Solar.R:太阳辐射量(lang)
- Wind:风速(mph)
- Temp:气温(华氏度)
- Month:月份(5-9)
你可以使用以下代码来加载这个数据集:
```r
data(airquality)
```
然后可以使用 `summary()` 函数来查看数据集的概要信息:
```r
summary(airquality)
```
这个函数会输出各个变量的一些统计信息,如最小值、最大值、中位数、平均数等等。你也可以使用其他函数来对数据进行分析和可视化。