C5.0决策树 - 名为exit的c50代码,值为1

Question

我收到以下错误

c50代码名为exit,值为1

我在Kaggle提供的巨大数据上这样做

# Importing datasets
train <- read.csv("train.csv", sep=",")

# this is the structure
  str(train)

输出: -

    'data.frame':   891 obs. of  12 variables:
 $ PassengerId: int  1 2 3 4 5 6 7 8 9 10 ...
 $ Survived   : int  0 1 1 1 0 0 0 0 1 1 ...
 $ Pclass     : int  3 1 3 1 3 3 1 3 3 2 ...
 $ Name       : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ...
 $ Sex        : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
 $ Age        : num  22 38 26 35 35 NA 54 2 27 14 ...
 $ SibSp      : int  1 1 0 1 0 0 0 3 0 1 ...
 $ Parch      : int  0 0 0 0 0 0 0 1 2 0 ...
 $ Ticket     : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
 $ Fare       : num  7.25 71.28 7.92 53.1 8.05 ...
 $ Cabin      : Factor w/ 148 levels "","A10","A14",..: 1 83 1 57 1 1 131 1 1 1 ...
 $ Embarked   : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...

然后我尝试使用C5.0 dtree

# Trying with C5.0 decision tree
library(C50)

#C5.0 models require a factor outcome otherwise error
train$Survived <- factor(train$Survived)

new_model <- C5.0(train[-2],train$Survived)

所以运行上面的行给了我这个错误

c50 code called exit with value 1

我无法弄清楚出了什么问题？我在不同的数据集上使用类似的代码,它工作正常.关于如何调试我的代码的任何想法？

-谢谢

Answer 1

对于任何感兴趣的人,可以在这里找到数据:http://www.kaggle.com/c/titanic-gettingStarted/data.我想你需要注册才能下载它.

关于你的问题,首先我认为你打算写

new_model <- C5.0(train[,-2],train$Survived)

接下来,请注意Cabin和Embarked列的结构.这两个因子具有空字符作为级别名称(选中levels(train$Embarked)).这是C50跌倒的地方.如果你修改你的数据

levels(train$Cabin)[1] = "missing"
levels(train$Embarked)[1] = "missing"

您的算法现在将运行而不会出现错误.

Answer 2

这是最终有效的方法：-

看完这篇文章后有了这个想法

library(C50)

test$Survived <- NA

combinedData <- rbind(train,test)

combinedData$Survived <- factor(combinedData$Survived)

# fixing empty character level names 
levels(combinedData$Cabin)[1] = "missing"
levels(combinedData$Embarked)[1] = "missing"

new_train <- combinedData[1:891,]
new_test <- combinedData[892:1309,]

new_model <- C5.0(new_train[,-2],new_train$Survived)

new_model_predict <- predict(new_model,new_test)

submitC50 <- data.frame(PassengerId=new_test$PassengerId, Survived=new_model_predict)
write.csv(submitC50, file="c50dtree.csv", row.names=FALSE)

这背后的直觉是，通过这种方式，训练和测试数据集将具有一致的因子水平。

Answer 3

以防万一.您可以查看错误

summary(new_model)

当变量名称中有特殊字符时,也会发生此错误.例如,如果变量名称中有"я"(来自俄语字母)字符,则会出现此错误.