CODEWITHSUNDEEP
rdecision-treerpart
user1965813
user1965813

Reputation: 671

How to force rpart to do exactly 1 Split

Having a problem similar to this, I am trying to force rpart to do exactly one split. Here is a toy example that reproduces my problem:

require(rpart)

y <- factor(c(1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0))
x1 <- c(12,18,15,10,10,10,20,6,7,34,7,11,10,22,4,19,10,8,13,6,7,47,6,15,7,7,21,7,8,10,15)
x2 <- c(318,356,341,189,308,236,290,635,550,287,261,472,282,262,1153,435,402,182,415,544,251,281,378,498,142,566,152,560,284,213,326)

data <- data.frame(y=y,x1=x1,x2=x2)
tree <-rpart(y~.,
              data=data,
              control=rpart.control(maxdepth=1, # at most 1 split
                                    cp=0, # any positive improvement will do
                                    minsplit=1,
                                    minbucket=1, # even leaves with 1 point are accepted
                                    xval=0)) # I don't need crossvalidation
length(tree$frame$var) #==1, so there are no splits

Isolating a single point should be possible (minbucket=1) and even the most marginal improvement (isolating one point always decreases the misclassification rate) should lead to the split being kept (cp=0).

Why does the result not include any splits? And how do I have to alter the code to always get exactly one split? Can it be that splits are not kept if both classify to the same factor output?

Upvotes: 1

Views: 4378

Answers (1)

ARobertson
ARobertson

Reputation: 2897

Change cp = 0 to cp = -1.

Apparently the cp for the first split (maxdepth = 3) is 0.0000000. So going negative allows it to show up with maxdepth = 1.

Upvotes: 3

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