Yes, in fact, changing the random_state might have an influence on the result. The docstring of the random_state parameter for the OneClassSVM seems incorrect though...
Albert On Thu, Aug 3, 2017 at 1:55 PM Nicolas Goix <goix.nico...@gmail.com> wrote: > @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 >> >> _______________________________________________ > 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
