如何在R中为决策树模型创建增益图表？

Question

I have created a decision tree model in R. The target variable is Salary, where we are trying to predict if the salary of a person is above or below 50k based on the other input variables

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

After that I tried to create a Gain chart by doing the following

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

By looking at the reference I am unable to understand how to use the ROCR package to build the chart by using the 'Prediction' function. Is this only for binary target variables? I get the error saying 'format of predictions is invalid'

Any help with this would be much appreciated to help me build a Gain chart for the above model. Thanks!!

  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

Convert the prediction to a vector, using c()

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)

Answer 2

This should work if you change

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

to

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

The gains chart wants to rank-order by model probability.