Peter,
Thanks for your answers. When I scale C by len(y_train), I get the
following error:
ValueError: C <= 0
Anne Dwyer
On Fri, Jul 12, 2013 at 3:34 PM, Peter Prettenhofer <
peter.prettenho...@gmail.com> wrote:
> Hi Anne,
>
> I would also expect that using uniform weights should result in the same
> solution as no weights -- but maybe there is an interaction with the C
> parameter... for this we would need to know more about the internals of
> libsvm and how it handles sample weights - try scaling C by
> ``len(y_train)`` and see what you get :-)
>
> PS: if you use the linear svm implemented by SGDClassifier(loss='hinge')
> you would also get this effect that uniform weights scale the
> regularization parameter.
>
> best,
> Peter
>
>
> 2013/7/12 Anne Dwyer <anne.p.dw...@gmail.com>
>
>> I have been using the sonar data set (I believe this is a sample data set
>> used in many demonstrations of machine learning.) It is a two class data
>> set with 60 features with 208 training examples.
>>
>> I have a questions about using sample weights in fitting the SVM model.
>>
>> When I fit the model using scaled data, I get a test error of 10.3%. When
>> I fit the model using a sample weight vector of 1/N, I get a test error of
>> 37%.
>>
>> Here is the code:
>>
>> w=np.ones(len(y_train))
>>
>> clf=svm.SVC(kernel='rbf', C=10, gamma=.01)
>> clf.fit(x_tr_scaled,y_train)
>>
>> score_scaled_tr=clf.score(x_tr_scaled,y_train)
>>
>> score_scaled_test=clf.score(x_te_scaled,y_test)
>>
>> w=w/sum(w)
>>
>> clf1=svm.SVC(kernel='rbf', C=10, gamma=.01, probability=True)
>>
>> clf1.fit(x_tr_scaled,y_train,sample_weight=w)
>>
>> print "Training score with sample weights is ", clf1.score(x_tr,y_train)
>>
>> print "Score with sample weights is", clf1.score(x_te_scaled,y_test)
>>
>> What am I doing wrong here?
>>
>> Also, when I tried this command:
>>
>> Pr=predict_proba(x_tr_scaled)
>>
>> I get the error that predict_proba is an undefined name. However, I got
>> it from this link:
>> http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
>>
>> Any help would be appreciated.
>>
>> Anne Dwyer
>>
>>
>>
>>
>>
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>
>
> --
> Peter Prettenhofer
>
>
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