如何在 R 中为决策树模型创建增益图？

Question

我在 R 中创建了一个决策树模型。目标变量是 Salary，我们试图根据其他输入变量来预测一个人的薪水是高于还是低于 50k

df<-salary.data 

train = sample(1:nrow(df), nrow(df)/2)
train = sample(1:nrow(df), size=0.2*nrow(df))
test = - train
training_data = df[train, ]
testing_data = df[test, ]

fit <- rpart(training_data$INCOME ~ ., method="class", data=training_data)##generate tree
testing_data$predictionsOutput = predict(fit, newdata=testing_data, type="class")##make prediction

之后我尝试通过执行以下操作来创建增益图表

# Gain Chart
pred <- prediction(testing_data$predictionsOutput, testing_data$INCOME)
gain <- performance(pred,"tpr","fpr")
plot(gain, col="orange", lwd=2)

通过查看参考资料，我无法理解如何使用 ROCR 包通过“预测”功能构建图表。这仅适用于二进制目标变量吗？我收到错误消息“预测格式无效”

对于帮助我为上述模型构建增益图表的任何帮助，我们将不胜感激。谢谢！！

  AGE          EMPLOYER     DEGREE             MSTATUS            JOBTYPE     SEX C.GAIN C.LOSS HOURS
1  39         State-gov  Bachelors       Never-married       Adm-clerical    Male   2174      0    40
2  50  Self-emp-not-inc  Bachelors  Married-civ-spouse    Exec-managerial    Male      0      0    13
3  38           Private    HS-grad            Divorced  Handlers-cleaners    Male      0      0    40

         COUNTRY INCOME
1  United-States  <=50K
2  United-States  <=50K
3  United-States  <=50K

Answer 1

如果你改变这应该可以工作

predict(fit, newdata=testing_data, type="class")

到

predict(fit, newdata=testing_data, type="prob")

增益图表希望按模型概率排序。

Answer 2

使用 c() 将预测转换为向量

library('rpart')
library('ROCR')
setwd('C:\\Users\\John\\Google Drive\\working\\R\\questions')
df<-read.csv(file='salary-class.csv',header=TRUE)

train = sample(1:nrow(df), nrow(df)/2)
train = sample(1:nrow(df), size=0.2*nrow(df))
test = - train
training_data = df[train, ]
testing_data = df[test, ]

fit <- rpart(training_data$INCOME ~ ., method="class", data=training_data)##generate tree
testing_data$predictionsOutput = predict(fit, 
                                         newdata=testing_data, type="class")##make prediction

# Doesn't work
# pred <- prediction(testing_data$predictionsOutput, testing_data$INCOME)
v <- c(pred = testing_data$predictionsOutput)
pred <- prediction(v, testing_data$INCOME)
gain <- performance(pred,"tpr","fpr")
plot(gain, col="orange", lwd=2)