Yep, thanks allo. I got the same answer mainly in metaoptimize as well. I
will be using that. Thanks!
best,
gene
On Tue, Jul 2, 2013 at 7:34 PM, Jaques Grobler <[email protected]>wrote:
> Didn't see your reply yet, Mathieu :)
> Thanks
>
>
> 2013/7/2 Jaques Grobler <[email protected]>
>
>> Ah when I looked further I see you got some answers here too
>>
>>
>> http://metaoptimize.com/qa/questions/13329/regression-task-trained-on-binary-labels
>>
>>
>>
>>
>> 2013/7/2 Jaques Grobler <[email protected]>
>>
>>> I would think that Logistic Regression[1] could apply here.. You can
>>> feed it binary labels and then it will act as a classifier that will return
>>> for each label the conditional class probability values .
>>>
>>> See [2] for scikit-learns implementation
>>>
>>> [1] http://en.wikipedia.org/wiki/Logistic_regression
>>>
>>> [2]
>>> http://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
>>>
>>> Hope it helps :)
>>>
>>>
>>>
>>> 2013/7/1 Gene Kogan <[email protected]>
>>>
>>>> I have a regression task where I have to assign a continous label
>>>> between 0 and 1, but my training set contains only binary labels, 0s and
>>>> 1s. Should I treat this as a classification problem and map the labels to
>>>> a continous line via some confidence metric (if it's available) or is there
>>>> a regression algorithm which can be trained on binary labels? What
>>>> scikits-learn methods will help me achieve this? Thanks!
>>>>
>>>> best,
>>>> gene
>>>>
>>>>
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>>
>
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