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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