[scikit-learn] OneClassSvm | Different results on different runs
albertthomas88 at gmail.com
Thu Aug 3 07:26:17 EDT 2017
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.
On Thu, Aug 3, 2017 at 12:41 PM Jaques Grobler <jaquesgrobler at gmail.com>
> 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
> 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 at python.org>:
>> 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?
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>> scikit-learn at python.org
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