@Doug: Sorry I was typing my previous response from my phone.
The snippet of code that I was talking about can be found at:
https://github.com/glouppe/scikit-learn/blob/master/sklearn/ensemble/forest.py#L93
Cheers,
Gilles
On Wednesday, 26 September 2012, Gilles Louppe <[email protected]> wrote:
> Hi,
>
> The ensemble classes handle the problem you describe already. Have a look
at the implementation of predict_proba of BaseForestClassifier in
ensemble.py if you want to do that yourself by hand.
>
> Hope this helps.
>
> Gilles
>
> On Wednesday, 26 September 2012, Mathieu Blondel <[email protected]>
wrote:
>>
>>
>> On Wed, Sep 26, 2012 at 3:52 AM, Doug Coleman <[email protected]>
wrote:
>>>
>>> If you examine the code, fit() "warms up" the optimization with some
>>> additional parameters, then calls _partial_fit(). partial_fit() just
>>> calls _partial_fit() directly. So, it looks like fit() and
>>> partial_fit() could take a `classes` parameter for SGDClassifier,
>>> rather than __init__. It seems a bit confused, actually, since
>>> SGDClassifier's __init__ takes a class_weight dict for doing
>>> cost-sensitive learning but then partial_fit() takes a classes
>>> vector--what if they contradict each other?
>>
>> partial_fit should behave exactly like fit if you call it only once. So,
for your use case, I would just use partial_fit with the classes parameter.
>> # The difference between fit and partial_fit is that fit erases the
previous model on subsequent calls whereas partial_fit starts from the
previous model.
>> Mathieu
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