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 >
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