I'm not that clued up about this specifically, but I'd think that using
the `class_weight` parameter for the svm classifier in sklearn should
achieve this.
Basically you can use this to penalize one class more than the other.
| class_weight : {dict, 'auto'}, optional
| Set the parameter C of class i to class_weight[i]*C for
| SVC. If not given, all classes are supposed to have
| weight one. The 'auto' mode uses the values of y to
| automatically adjust weights inversely proportional to
| class frequencies.
Can someone (please :) )confirm if I'm on the right track with this?
Hope this helps,
Jaques
2012/6/5 Emily Wong <[email protected]>
> Hi,
> I'm new to scikit and machine learning in general and I trying to use svm
> to find the best way to classify a dataset containing lots of negatives. I
> was wondering if there is a way to increase the penalty factor associated
> with misclassification of the positive class?
> Thanks,
> Emily
>
>
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