[R] learning decision trees with one's own scoring functins

[Prof Brian Ripley]

Please do study the packages you mention a great deal more carefully 
before posting such negative remarks about them.

In particular, rpart is already fully user-extensible (and comes with a 
worked example), and both packages are supplied in source code on CRAN.

On Fri, 26 Aug 2005, zhihua li wrote:

> Hi netters,
>
> I want to learn a decision tree from a series of instances (learning data). 
> The packages
> tree or rpart can do this quite well, but the scoring functions (splitting 
> criteria) are
> fixed in these packages, like gini or something. However, I'm going to use 
> another scoring
> function. 
> At first I wanna modify the R code of tree or rpart and put my own scoring 
> function in. But it seems that tree and rpart perform the splitting procedure 
> by calling external C functions, which I have no access to. So do I have to 
> write R code from scratch to build the tree with my own scoring functions? 
> It's a really tough task. Or r there other R packages that can do similar 
> things with more flexible and extensible code?
>
> Thanks a lot!
>
>

-- 
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)
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