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 > >
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