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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