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

Question 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.

Question 2

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.

Question 3

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.

Question 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.

Question 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.

Question 6

I also got the same error, but it was because of some illegal characters in the factor levels of one the columns.

I used make.names function and corrected the factor levels:

levels(FooData$BarColumn) <- make.names(levels(FooData$BarColumn))

Then the problem was resolved.