🚀 go-pugleaf

Groups trimmed to sci.stat.math, aus.stats.s.

 - the question lacks some basic parameters which 
would limit the options -

On 8 Jun 2002 12:08:15 +1000, demiurg@tau.ac.il (Alexander Sirotkin)
wrote:

> 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). 

That's bad if there are just two choices, with equal Ns.
Just how bad is it?
 
> The data : 562 real predictor variables and one 
> independent variable, factor (with 86 types).

85 dummy variables?  Or, ordered in a sensible way to
be 3 or 4 dummies, or 15?

> 
> Quite a lot of missing values...

Scattered randomly across 600 variables?
across 500?  5000?  5 million cases?
What do you lose if you drop all Subjects
with missing? - all variables with missing?
The numerically comprehensive way to try to 
deal with missing is to create a dummy variable 
for Missing/Not .  For a real Prediction equation,
for the future, you seldom see that.  It is not
going to be worth it except for important variables
that *might* , in real life, end up Missing.

> Any ideas what should I try (data preprocessing, etc...)
> to reduce error rate ?

I like to create composite scores that are meaningful.
A reference that was posted recently suggests that 
variable selection might be best achieved by generating
equations in relatively small sub-samples, so that the
validation can magnify the error rate.  That strikes me
as an intriguing suggestion -- I have always been very
skeptical about stepwise variables selection.

There's hardly a clue about what makes up these data, 
so my 'ideas'  are limited.  

> [ comp.ai is moderated.  
 < snipped >

-- 
Rich Ulrich, wpilib@pitt.edu
http://www.pitt.edu/~wpilib/index.html