On 08/13/2012 08:29 PM, Abhi wrote: > Andreas Müller <amueller@...> writes: > >> Alternatively you could look at the output of "decision_function" in > LinearSVC. >> These do not represent probabilities, though. >> >> Andy >> > > Hi Andy, thanks for pointing me towards that. I looked around online but I'm > still not sure how I can use the decision_function method to determine how > good > the match was (i.e. how confident LinearSVC's prediction that the input is > in > this category is). Could you shed some light on this? > The problem with the values are that they are not normalized, so the range is hard to interpret. If you have a two-class problem, higher means more confident for positive class and lower means more confident for negative class. This value can be used for example to plot roc-curves and do precision-recall trade offs.
I am using "confident" here in an informal way. If you want real probabilities, try LogisticRegression, as I said in my other mail. Cheers, Andy ------------------------------------------------------------------------------ Live Security Virtual Conference Exclusive live event will cover all the ways today's security and threat landscape has changed and how IT managers can respond. Discussions will include endpoint security, mobile security and the latest in malware threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
