@albertcthomas isn't there some randomness in SMO which could influence the result if the tolerance parameter is too large?
On Aug 3, 2017 1:28 PM, "Albert Thomas" <albertthoma...@gmail.com> wrote: > Hi Abhishek, > > Could you provide a small code snippet? I don't think the random_state > parameter should influence the result of the OneClassSVM as there is no > probability estimation for this estimator. > > Albert > > On Thu, Aug 3, 2017 at 12:41 PM Jaques Grobler <jaquesgrob...@gmail.com> > wrote: > >> Hi, >> >> The random_state parameter is used to generate a pseudo random number >> that is used when shuffling your data for probability estimation >> >> The seed of the pseudo random number generator to use when shuffling the >> data for probability estimation. >> A seed can be provided to control the shuffling for reproducible behavior. >> >> Also, from the SVM docs >> <http://scikit-learn.org/stable/modules/svm.html#svm-outlier-detection> >> >> The underlying LinearSVC >>> <http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html#sklearn.svm.LinearSVC> >>> implementation uses a random number generator to select features when >>> fitting the model. It is thus not uncommon, to have slightly different >>> results for the same input data. If that happens, try with a smaller *tol >>> *parameter. >> >> >> Hope that helps >> >> 2017-08-03 12:15 GMT+02:00 Abhishek Raj via scikit-learn < >> scikit-learn@python.org>: >> >>> Hi, >>> >>> I am using one class svm for developing an anomaly detection model. I >>> observed that different runs of training on the same data set outputs >>> different accuracy. One run takes the accuracy as high as 98% and another >>> run on the same data brings it down to 93%. Googling a little bit I found >>> out that this is happening because of the random_state >>> <http://scikit-learn.org/stable/modules/generated/sklearn.utils.check_random_state.html> >>> parameter >>> but I am not clear of the details. >>> >>> Can anyone expand on how is the parameter exactly affecting my training >>> and how I can figure out the best value to get the model with best accuracy? >>> >>> Thanks, >>> Abhishek >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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