2012/8/26 Andreas Mueller <[email protected]>: > Hi David. > I don't think this is possible. > Getting the probability is already a bit of a hack. > The LibSVM uses cross-validation and Platt-scaling for that afaik. > > I am not so much into the statistics side but I don't know any classfier > that gives you confidences, except maybe ensembles.
A sound, non parametric but computationally expensive way to get this kind of information (confidence intervals on the estimated parameters or predicted probability estimate) would be to bootstrap: resample n_samples out of n_samples with replacement from your training dataset n_bootstraps times and fit a model for each bootstrap and store the values of the fitted parameters or predicted probability estimates in a array and then compute 95% intervals by taking quantiles of those collected estimates. -- Olivier http://twitter.com/ogrisel - http://github.com/ogrisel ------------------------------------------------------------------------------ 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
