🚀 go-pugleaf

Have you considered CART? A friend of mine swears by it:

  http://www.google.com/search?q=classification+and+regression+trees+cart

Bjarne

In comp.ai Alexander Sirotkin <demiurg@tau.ac.il> wrote:
: I managed to reduce the error rate by reducing the "minimal node deviance"
: parameter of the partitioning algorithm. But it seems to me that this method 
: is wrong, that it just makes a bigger tree (with more terminal nodes) which 
: just memorizes the whole input.
: But on the other hand, S-plus does use a different test set, so that there 
: can not be generalization problems...

: Very confuzing...

: demiurg@tau.ac.il (Alexander Sirotkin) wrote in message news:<adrouf$h81$1@mulga.cs.mu.OZ.AU>...
:> Question : How to improve (reduce) decision tree 
:> classification model error rate?
:> 
:> I'm trying to build some decision tree classification
:> model (using S-plus) and keep getting extremely bad
:> results (error rate around 0.5). 
:> 
:> The data : 562 real predictor variables and one 
:> independent variable, factor (with 86 types).
:> 
:> Quite a lot of missing values...
:> 
:> Any ideas what should I try (data preprocessing, etc...)
:> to reduce error rate ?
:>

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