Using rpart: how to get more variability in forecasts?

Question

I use the rpart package as follows:

model <- rpart(totalUSD ~ ., data = df.train) 

I noticed that over 80 thousand lines of rpart summarizes forecasts for three groups , as shown in the figure below:

I see several configuration options for the rpart method ; however, I do not quite understand them.

Is there a way to configure rpart to make more forecasts (instead of three) ; not so harsh groups, but more levels between them?

The reason I'm asking for is because my cost estimate looks pretty simplified as it only returns one of three numbers!

Here is an example of my data:

 structure(list(totalUSD = c(9726.6, 730.14, 750, 200, 60.49, 310.81, 151.23, 145.5, 3588.13, 400), durationDays = c(730, 724, 730, 189, 364, 364, 364, 176, 730, 1095), familySize = c(4, 1, 2, 1, 3, 2, 1, 1, 4, 4), serviceName = c("Service5", "Service6", "Service9", "Service4", "Service1", "Service2", "Service1", "Service3", "Service7", "Service8"), homeLocationGeoLat = c(37.09024, 10.691803, 37.09024, 35.86166, 55.378051, 35.86166, 51.165691, -30.559482, -30.559482, 41.87194), homeLocationGeoLng = c(-95.712891, -61.222503, -95.712891, 104.195397, -3.435973, 104.195397, 10.451526, 22.937506, 22.937506, 12.56738), hostLocationGeoLat = c(55.378051, 37.09024, 55.378051, 55.378051, 37.09024, 1.352083, 55.378051, 37.09024, 23.424076, 1.352083), hostLocationGeoLng = c(-3.435973, -95.712891, -3.435973, -3.435973, -95.712891, 103.819836, -3.435973, -95.712891, 53.847818, 103.819836), geoDistance = c(6838055.10555534, 4532586.82063172, 6838055.10555534, 7788275.0443749, 6838055.10555534, 3841784.48282769, 1034141.95021832, 14414898.8246973, 6856033.00945242, 10022083.1525388)), .Names = c("totalUSD", "durationDays", "familySize", "serviceName", "homeLocationGeoLat", "homeLocationGeoLng", "hostLocationGeoLat", "hostLocationGeoLng", "geoDistance"), row.names = c(25601L, 6083L, 24220L, 20235L, 8372L, 456L, 8733L, 27257L, 15928L, 24099L), class = "data.frame") 

Answer 1

If you really need a complex tree structure, try the following:

 library(rpart) fit = rpart(totalUSD ~ ., data = df.train, control = rpart.control(cp = 0)) 

Basically, each split is checked when cp = 0 , regardless of the improvement in the split. You get a really complex model with this, but you have 80k observations, so set minsplit or minbucket to the number that minbucket .

I used this strategy in the random implementation of the forest in which I work. Caution that the calculation time may increase significantly.