R help - Mar 2003 - rpart returning only 1 node

Hi,

This may actually be a theoretical question.

When I tried to do the following:

##########################################################
> colnames(rating.adclms)
 [1] "usage"    "mileage"  "sex"     
"excess"   "ncd"
 [6] "primage"  "minage"   "drivers" 
"district" "cargroup"
[11] "car.age"  "adclms"   "days"   
> rating.r1 <- rpart(adclms ~ ., data = rating.adclms, 
+                                method =
"class")
> rating.r1
n= 140602 

node), split, n, loss, yval, (yprob)
      * denotes terminal node

1) root 140602 3792 0 (9.730303e-01 2.506365e-02 1.834967e-03 
7.112274e-05) *
##########################################################

Should I set the costs in rpart()?  I'm kind of surprised to see it only 
return 1 node for the tree.

-- 
Cheers,

Kevin

------------------------------------------------------------------------------
/* Time is the greatest teacher, unfortunately it kills its students */

--
Ko-Kang Kevin Wang
Master of Science (MSc) Student
Department of Statistics
University of Auckland
New Zealand
Homepage: http://www.stat.auckland.ac.nz/~kwan022
Ph: 373-7599
    x88475 (City)
    x88480 (Tamaki)

On Mon, 10 Mar 2003, Ko-Kang Kevin Wang wrote:
> Hi,
> 
> This may actually be a theoretical question.
> 
> When I tried to do the following:
> 
> ##########################################################
> > colnames(rating.adclms)
>  [1] "usage"    "mileage"  "sex"     
"excess"   "ncd"
>  [6] "primage"  "minage"   "drivers" 
"district" "cargroup"
> [11] "car.age"  "adclms"   "days"    
> > rating.r1 <- rpart(adclms ~ ., data = rating.adclms, 
> +                                method = "class")
> > rating.r1
> n= 140602 
> 
> node), split, n, loss, yval, (yprob)
>       * denotes terminal node
> 
> 1) root 140602 3792 0 (9.730303e-01 2.506365e-02 1.834967e-03 
> 7.112274e-05) *
> ##########################################################
> 
> Should I set the costs in rpart()?  I'm kind of surprised to see it
only
> return 1 node for the tree.
Why are you surprised?  One class has 97% of the examples, and it may be
impossible to get a single split that makes a worthwhile improvement (1%)
in classification.  You probably want to set cp (an argument to
rpart.control).

You could use losses, but I would use weighted sub-sampling of the
training set. See my 1996 book on Pattern Recognition and Neural Networks
for the theory and the practical details.

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595