在 rpart 和随机森林中处理跳过

Question

I have a dataset containing 10 categorical variables. Each of these has missing values coded as (-9, -6, -3, -2, -1). I want to create 1 column that takes the mean of these 10 variables excluding the negative values. I can collapse the negative values into NA and then median impute them but I need to retain -6 since -6 implies that the person skipped the question because it does not apply to them. For instance, parental relationship quality does not apply to single parents. I ultimately want to use this variable as a predictor in my random forest model so I am not sure how to handle -6 in this case. One way that I could think of is to impute each of the 10 variables as follows (Let's say that the 10 variables are a1 to a10):

missing_categs <- c(-9, -3, -2, -1)

df[df$a1%in%missing_categs,]$a1 <- assign median value of a1

After the above step, I calculate the average of a1 to a10. The ones that yield "-6" are the ones that pertain to single parents (which means it does not apply to them). then, I convert -6 to NA. So, now I have average values and one NA. Can rpart and random forest models handle NA? Other better alternative solutions are most welcome. Thanks in advance!

Answer 1

Can rpart and random forest models handle NA?

I do not know what you mean with handle . If you mean that you can use NA in the predictors than the answer is yes for rpart

> library(rpart)
> df <- data.frame(c(1, 2, NA), c(4, 5, 6))
> rpart(df, na.action=na.pass)
n= 3 

node), split, n, deviance, yval
      * denotes terminal node

but no for randomForest

> library(randomForest)
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
> df <- data.frame(c(1, 2, NA), c(4, 5, 6))
> randomForest(df, na.action=na.pass)
Error in randomForest.default(df, na.action = na.pass) : 
  NA not permitted in predictors

If you mean handle that they are able to deal with them in some manner, for example by giving them a function, than the answer is yes for both .

rpart and randomForest have the parameter na.action which you can use. See here for rpart and here for randomForest .

The default for rpart na.action is na.rpart which deletes "all observations for which y is missing" and "those in which one or more predictors are missing" are kept.

The default for randomForest na.action is na.fail which returns the given data structure unaltered if no NA 's are found, and if at least one NA is found it "signals an error" .