I think that should be Tree.apply, not apply_Tree. I.e. I guess you want to
use something like (unverified):
for leaf_ind, values in groupby(sorted(zip(regressor.tree_.apply(X_train),
y_train)), operator.itemgetter(0)):
regressor.tree_.values[leaf_ind, ...] = np.median(list(values))
On 23 June 2014 07:57, Peter Prettenhofer <[email protected]>
wrote:
> 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
>>
>>
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