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Decission Tree方包预测错误 - 级别不匹配

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我正在使用party包在R中构建CART回归树模型，但是当我尝试将模型应用于测试数据集时，我收到错误消息，指出级别不匹配 .

过去一周我一直在阅读论坛上的主题，但仍无法找到解决问题的正确方法 . 所以我在这里使用我编写的假例子重新发布这个问题 . 有人可以帮助解释错误信息并提供解决方案吗？

我的训练数据集有大约1000条记录，测试数据集大约有150条 . 两个数据集中都没有NA或空白字段 .

在派对包下使用ctree的我的CART模型是：

mytree < - ctree（Rate~Bank Product Salary，data = data_train）

data_train示例：

Rate  Bank  Product  Salary    
1.5    A     aaa     100000
0.6    B     abc      60000
3      C     bac      10000
2.1    D     cba      50000
1.1    E     cca      80000

data_test示例：

Rate  Bank  Product   Salary
2.0    A     cba       80000
0.5    D     cca      250000
0.8    E     cba      120000
2.1    C     abc       65000

levels(data_train$Bank) : A, B, C, D, E

levels(data_test$Bank): A,D,E,C

我尝试使用以下代码设置为相同级别：

>is.factor(data_test$Bank)

 TRUE 
(Made sure Bank and Products are factors in both datasets)
>levels(data_test$Bank) <-union(levels(data_test$Bank), levels(data_train$Bank))

> levels(data_test$product)<-union(levels(data_test$product),levels(data_train$product))

但是，当我尝试在测试数据集上运行预测时，我收到以下错误：

> fit1<- predict(mytree,newdata=data_test)

Error in checkData(oldData, RET) : 
  Levels in factors of new data do not match original data

我也尝试了以下方法，但它改变了我的测试数据集的字段...：

levels（data_test $ Bank）<-levels（data_train $ Bank）

data_test表被更改：

Rate  Bank(altered)  Bank (original)   
2.0    A              A      
0.5    B              D      
0.8    C              E      
2.1    D              C
r decision-tree prediction party

1 回答

  • 1

    您可以尝试使用可比较的级别重建因子，而不是为现有因子分配新级别 . 这是一个例子：

    # start the party
library(party)

# create training data sample
data_train <- data.frame(Rate = c(1.5, 0.6, 3, 2.1, 1.1),
                         Bank = c("A", "B", "C", "D", "E"),
                         Product = c("aaa", "abc", "bac", "cba", "cca"),
                         Salary = c(100000, 60000, 10000, 50000, 80000))

# create testing data sample
data_test <- data.frame(Rate = c(2.0, 0.5, 0.8, 2.1),
                         Bank = c("A", "D", "E", "C"),
                         Product = c("cba", "cca", "cba", "abc"),
                         Salary = c(80000, 250000, 120000, 65000))

# get the union of levels between train and test for Bank and Product
bank_levels <- union(levels(data_test$Bank), levels(data_train$Bank))
product_levels <- union(levels(data_test$Product), levels(data_train$Product))

# rebuild Bank with union of levels
data_test$Bank <- with(data_test, factor(Bank, levels = bank_levels)) 
data_train$Bank <- with(data_train, factor(Bank, levels = bank_levels)) 

# rebuild Product with union of levels
data_test$Product <- with(data_test, factor(Product, levels = product_levels)) 
data_train$Product <- with(data_train, factor(Product, levels = product_levels)) 

# fit the model
mytree <- ctree(Rate ~ Bank + Product + Salary, data = data_train)

# generate predictions
fit1 <- predict(mytree, newdata = data_test)

> fit1
     Rate
[1,] 1.66
[2,] 1.66
[3,] 1.66
[4,] 1.66
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