来自rpart的混淆矩阵

Question

我不能为我的生活弄清楚如何计算rpart上的混淆矩阵.

这是我做的:

set.seed(12345)
UBANK_rand <- UBank[order(runif(1000)), ]
UBank_train <- UBank_rand[1:900, ]
UBank_test  <- UBank_rand[901:1000, ]


dim(UBank_train)
dim(UBank_test)

#Build the formula for the Decision Tree
UB_tree <- Personal.Loan ~ Experience + Age+ Income +ZIP.Code + Family + CCAvg + Education

#Building the Decision Tree from Test Data
UB_rpart <- rpart(UB_tree, data=UBank_train)

现在,我想我会做类似的事情

table(predict(UB_rpart, UBank_test, UBank_Test$Default))

但这并没有给我一个混乱的矩阵.

Answer 1

您没有提供可重现的示例,因此我将创建一个合成数据集:

set.seed(144)
df = data.frame(outcome = as.factor(sample(c(0, 1), 100, replace=T)),
                x = rnorm(100))

predict具有rpart模型的函数type="class"将返回每个观察的预测类.

library(rpart)
mod = rpart(outcome ~ x, data=df)
pred = predict(mod, type="class")
table(pred)
# pred
#  0  1 
# 51 49 

最后,您可以通过table在预测和真实结果之间运行来构建混淆矩阵:

table(pred, df$outcome)
# pred  0  1
#    0 36 15
#    1 14 35