🚀 go-pugleaf

Hi.

Thanks for the response.

More details:
10000 records, with 562 independent variables and one dependent
variables. All 562 varriables are equally important, so that variable
selection
is meaningless, only dimension reduction may make sense (but PCA does
not,
due to missing values problem).

The dependent variables is factor with 86 different values.
Hence the error rate ot 0.5 is really terrible.

Removing all records with missing values is out of the question -
there are too
many of them.

BTW, using spepwise method (if I get the term right - dividing the
sample to say 4 subsamples
and build a classification model for each one), does help...




Rich Ulrich <wpilib@pitt.edu> wrote in message news:<mka9gukh228jdm993ht1erdo793j6f52ig@4ax.com>...
> 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 >