On Jun 7, 2013, at 11:57 PM, Ken Geis <[email protected]> wrote:
> On May 23, 2013, at 5:03 AM, Gilles Louppe <[email protected]> wrote:
>>> So I'd like to contribute a simple MAE criterion that would be efficient
>>> for random splits (i.e. O(n) given a single batch update.) Is the direction
>>> forward for something like this to hard-code more criteria in _tree.pyx, or
>>> would it be better to approach some modularity and allow a Criterion object
>>> to be passed in?
>>
>> At the moment, adding a criterion require writing a new class
>> implementing the Criterion interface defined in _tree.pyx. It should
>> then be pluggable as is without any other change to the code.
>
> For everyone's information, I'm starting to have success doing this. To pass
> in my own criterion, I first need to register it like this:
>
>
> import sklearn.tree.tree
>
> class MAE(Criterion):
> #implementation here
> tree.REGRESSION['mae'] = MAE()
>
> modeler = ExtraTreesRegressor(criterion='mae', …)
>
update:
import sklearn.tree.tree
class MAE(RegressionCriterion):
#implementation here
tree.REGRESSION['mae'] = MAE
modeler = ExtraTreesRegressor(criterion='mae', …)
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