Hi Peter,
Thanks for your answer and the interesting thread you linked
(I should have catch it before sending my email ;-)).
I agree that Platt scaling would be of help. As far as I understand
in the multiclass case [0] it should be like:
s = clf.decision_function(x)
p(s) = exp(A*s+B) / exp(A*s+B).sum()
where s is, say, the distance from the separating plane for the
example x, while A and B should be estimated by minimizing the
loss of interest on a new set of examples {(x_1,y_1),...,(x_n,y_n)}.
Best,
Emanuele
[0]: http://en.wikipedia.org/wiki/Multinomial_logit
On 07/09/2012 10:53 AM, Peter Prettenhofer wrote:
> Hi Emanuele,
>
> 2012/7/9 Emanuele Olivetti <[email protected]>:
>> Hi,
>>
>> I'm not expert in Stochastic Gradient Descent but I'm wondering why
>> predict_proba() is not available in the multiclass case. Precisely
>> I get this (sklearn v0.11):
>> ----
>> NotImplementedError: predict_(log_)proba only supported for binary
>> classification
>> ----
>> I see that the multiclass case is supported through a "one versus all"
>> scheme.
>> Is this one the motivation for the lack of multiclass probabilities?
> Precisely, I'm not an expert on probability calibration thus I choose
> to not support predict_proba in the OVA case. We have started working
> on a proper multinomial logistic regression model for SGDClassifier
> but progress has stalled a bit. Having said that: liblinear's Logistic
> Regression model simply normalizes the individual OVA probabilities.
>
>> Are there solutions available?
> Take a look at this thread on the mailing list
> http://sourceforge.net/mailarchive/message.php?msg_id=29413195
>
> The simplest "solution" is to monkey patch SGDClassifier such that it
> normalizes the OVA probabilities as liblinear does.
>
> Maybe a better solution is to use Platt scaling (do we have that in scikit?)
>
> best,
> Peter
>
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