Hi,

In the R-Help history there have been similar questions to yours. As a
starting point you can check this:

http://tolstoy.newcastle.edu.au/R/e2/help/07/01/9138.html

Regrads,
Carlos.

On Thu, Jul 22, 2010 at 6:37 PM, David Shin <dshin@jumptrading.com> wrote:

> I'd like to train a decision tree on a set of weighted data points.  I
> looked into the rpart package, which builds trees but doesn't seem to offer
> the capability of weighting inputs.  (There is a weights parameter, but it
> seems to correspond to output classes rather than to input points).
>
> I'm making do for now by preprocessing my input data by adding multiple
> instances of each data point corresponding to its weight before feeding to
> rpart.  But I worry this tricks the cross-validation phase of the rpart
> building process into thinking a model generalizes better than it really
> does.  This is because a heavily-weighted point can be included in both the
> training and testing set of a cross validation split.
>
> Is there a better way to achieve my goal?
>
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