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r - R中的简单决策树-插入符号包的奇怪结果

问题描述

我正在尝试使用 caret 包将一个简单的决策树应用于以下数据集，数据为：

> library(caret)
> mydata <- read.csv("https://stats.idre.ucla.edu/stat/data/binary.csv")
> mydata$rank <- factor(mydata$rank)
  # create dummy variables
> X = predict(dummyVars(~ ., data=mydata), mydata)
> head(X)

    A matrix: 6 × 7 of type dbl     
admit   gre gpa rank.1  rank.2  rank.3  rank.4
    0   380 3.61    0        0        1      0
    1   660 3.67    0        0        1      0
    1   800 4.00    1        0        0      0
    1   640 3.19    0        0        0      1
    0   520 2.93    0        0        0      1
    1   760 3.00    0        1        0      0

拆分为训练和测试集：

> trainset <- data.frame(X[1:300,])
> testset <- data.frame(X[301:400,])

现在应用决策树：

> tree <- train(factor(admit) ~., data = trainset, method = "rpart")
> tree

CART 

300 samples
  6 predictor
  2 classes: '0', '1' 

No pre-processing
Resampling: Bootstrapped (25 reps) 
Summary of sample sizes: 300, 300, 300, 300, 300, 300, ... 
Resampling results across tuning parameters:

 cp          Accuracy   Kappa    
0.01956522  0.6856163  0.1865179
0.03260870  0.6888378  0.1684015
0.08695652  0.7080434  0.1079462

Accuracy was used to select the optimal model using the largest value.
The final value used for the model was cp = 0.08695652.

我得到NaN了可变的重要性！为什么？

> varImp(tree)$importance

A data.frame: 6 × 1     Overall
<dbl>
gre NaN
gpa NaN
rank.1  NaN
rank.2  NaN
rank.3  NaN
rank.4  NaN

而在预测中，决策树只输出一个类，即 0 类，为什么？我的代码有什么问题？提前致谢。

> y_pred <- predict(tree ,newdata=testset)
> y_test <- factor(testset$admit)
> confusionMatrix(y_pred, factor(y_test))

Confusion Matrix and Statistics

      Reference
Prediction  0  1
         0 65 35
         1  0  0

           Accuracy : 0.65            
             95% CI : (0.5482, 0.7427)
No Information Rate : 0.65            
P-Value [Acc > NIR] : 0.5458          

              Kappa : 0               

Mcnemar's Test P-Value : 9.081e-09       

        Sensitivity : 1.00            
        Specificity : 0.00            
     Pos Pred Value : 0.65            
     Neg Pred Value :  NaN            
         Prevalence : 0.65            
     Detection Rate : 0.65            
 Detection Prevalence : 1.00            
  Balanced Accuracy : 0.50            

   'Positive' Class : 0

标签： rmachine-learningdecision-treer-caret

解决方案

我无法回答您的问题，但我可以向您展示我用来计算决策树的方式：

library(data.table)
library(tidyverse)
library(caret)
library(rpart)
library(rpart.plot)

# Reading data into data.table
mydata <- fread("https://stats.idre.ucla.edu/stat/data/binary.csv")

# converting rank and admit to factors
mydata$rank  <- as.factor(mydata$rank)
mydata$admit <- as.factor(mydata$admit)

# creating train and test data
t_index  <- createDataPartition(mydata$admit, p=0.75, list=FALSE)
trainset <- mydata[t_index,]
testset  <- mydata[-t_index,]

# calculating the model using rpart
model <- rpart(admit ~ .,
               data = trainset,
               parms = list(split="information"),
               method = "class")

# plotting the decision tree
model %>%
  rpart.plot(digits = 4)

# get confusion matrix
model %>%
  predict(testset, type = "class") %>%
  table(testset$admit) %>%
  confusionMatrix()

也许这对您有所帮助。

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