Hi James,
if you look at the LAD loss function in the gradient_boosting module you
can find an example how to do it. Basically, you need to update the values
array in the Tree extension type. Tree.apply_Tree(x_train) gives you the
training instances in each leaf.
HTH,
Peter
Am 23.06.2014 13:48 schrieb "James McMurray" <[email protected]>:
> Hi,
>
> I want to use the decision tree regressor to predict using the median of
> the resulting subset from the tree, rather than the mean?
>
> Is there a simple way to do this?
>
> I looked at the code, but in sklearn/tree/tree.py, the only relevant line
> is:
> proba = self.tree_.predict(X)
>
> Where the prediction is already done (presumably in the Cython code), I
> don't have experience with Cython so I'm not sure how to modify _tree.pyx
> to do this.
>
> Many thanks,
> James McMurray
>
>
> ------------------------------------------------------------------------------
> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> Find What Matters Most in Your Big Data with HPCC Systems
> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> http://p.sf.net/sfu/hpccsystems
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
------------------------------------------------------------------------------
HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
Find What Matters Most in Your Big Data with HPCC Systems
Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
Leverages Graph Analysis for Fast Processing & Easy Data Exploration
http://p.sf.net/sfu/hpccsystems
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
