C5.0 decision tree - c50 code called exit with value 1

问题:

I am getting the following error

c50 code called exit with value 1

I am doing this on the titanic data available from Kaggle

# Importing datasets train <- read.csv("train.csv", sep=",")  # this is the structure   str(train) 

Output :-

    '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 ... 

Then I tried using 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) 

So running the above lines gives me this error

c50 code called exit with value 1 

I'm not able to figure out what's going wrong? I was using similar code on different dataset and it was working fine. Any ideas about how can I debug my code?

-Thanks

回答1:

For anyone interested, the data can be found here: http://www.kaggle.com/c/titanic-gettingStarted/data. I think you need to be registered in order to download it.

Regarding your problem, first of I think you meant to write

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

Next, notice the structure of the Cabin and Embarked Columns. These two factors have an empty character as a level name (check with levels(train$Embarked)). This is the point where C50 falls over. If you modify your data such that

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

your algorithm will now run without an error.

回答2:

Here is what worked finally:-

Got this idea after reading this post

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) 

The intuition behind this is that in this way both the train and test data set will have consistent factor levels.

回答3:

Just in case. You can take a look to the error by

summary(new_model) 

Also this error occurs when there are a special characters in the name of a variable. For example, one will get this error if there is "я"(it's from Russian alphabet) character in the name of a variable.

回答4:

I had the same error, but I was using a numeric dataset without missing values.

After a long time, I discovered that my dataset had a predictive attribute called "outcome" and the C5.0Control use this name, and this was the error cause :'(

My solution was changing the column name. Other way, would be create a C5.0Control object and change the value of the label attribute and then pass this object as parameter for the C50 method.

回答5:

I also struggled some hours with the same Problem (Return code "1") when building a model as well as when predicting. With the hint of answer of Marco I have written a small function to remove all factor levels equal to "" in a data frame or vector, see code below. However, since R does not allow for pass by reference to functions, you have to use the result of the function (it can not change the original dataframe):

removeBlankLevelsInDataFrame <- function(dataframe) {   for (i in 1:ncol(dataframe)) {     levels <- levels(dataframe[, i])     if (!is.null(levels) && levels[1] == "") {       levels(dataframe[,i])[1] = "?"     }   }   dataframe }  removeBlankLevelsInVector <- function(vector) {   levels <- levels(vector)   if (!is.null(levels) && levels[1] == "") {     levels(vector)[1] = "?"   }   vector } 

Call of the functions may look like this:

trainX = removeBlankLevelsInDataFrame(trainX) trainY = removeBlankLevelsInVector(trainY) model = C50::C5.0.default(trainX,trainY) 

However, it seems, that C50 has a similar Problem with character columns containing an empty cell, so you will have probably to extend this to handle also character attributes if you have some.

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