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r - problem with decision tree applied to dataset

问题描述

I was testing to program a decision tree by using R and decided to use the car dataset from UCI, available here.

According to the authors it has 7 attributes which are:

CAR                      car acceptability
   . PRICE                  overall price
   . . buying               buying price
   . . maint                price of the maintenance
   . TECH                   technical characteristics
   . . COMFORT              comfort
   . . . doors              number of doors
   . . . persons            capacity in terms of persons to carry
   . . . lug_boot           the size of luggage boot
   . . safety               estimated safety of the car

so I want to use a DT as a classifier for getting the car acceptability considering the buying price, maint, comfort, doors, persons, lug_boot and safety.

First of all I extracted the first column as the dependent variable and then I noticed that the data was arrange in order; depending on the value of the first column (very high, high, medium,low). For this reason, I decided to shuffle the data. My code is the following:

car_data<-read.csv("car.data")
library(C50)
set.seed(12345)
car_data_rand<-car_data[order(runif(1727)),]
car_data<-car_data_rand
car_data_train<-car_data[1:1500,]
car_data_test<-car_data[1501:1727,]
answer<-data_train$vhigh
answer_test<-data_test$vhigh
#deleting the dependent variable or y from the data
car_data_train$vhigh<-NULL
car_data_test$vhigh<-NULL
car_model<-C5.0(car_data_train,answer)
summary(car_model)

Here I get an awful error:

Evaluation on training data (1500 cases):

        Decision Tree   
      ----------------  
      Size      Errors  

         7  967(64.5%)   <<

What am I doing wrong?

标签： rdecision-tree

解决方案

  1. 在你的代码中间你有data_trainanddata_test而不是car_data_trainand car_data_test。

  2. 虽然错误很高，但没有任何问题。注意

1 - table(answer) / length(answer)
# answer
#      high       low       med     vhigh 
# 0.7466667 0.7566667 0.7426667 0.7540000

这意味着如果你天真地总是猜“低”，你的错误将是 75.6%。所以，有一个改进，大约 11.1%。它有点低的事实意味着预测变量不是很好。

  1. 最后，存在不一致：您说要对汽车的可接受性进行建模，而您的代码是关于buying变量的。现在修复它只会导致 1.1% 的错误。但是，在这种情况下，您的样本非常不平衡：

1 - table(answer) / length(answer)
# answer
#       acc      good     unacc     vgood 
# 0.7773333 0.9600000 0.3020000 0.9606667

也就是说，通过总是unacc再次猜测你可能已经得到了 30.2% 的错误。然而，29.1% 的改进显然更大。

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