So basically you would not do grid search, and just look at the proportion of
outliers in training and test datasets.
But what if you have so few examples of anomalies, that you want to keep them
all in the test dataset. In that case, how would you go about finding the best
value of nu?
Thank you,
From: Albert Thomas [mailto:[email protected]]
Sent: Saturday, September 06, 2014 5:35 AM
To: [email protected]
Subject: Re: [Scikit-learn-general] one-class SVM with limited number of samples
Hi Roberto,
One possible way to tune the hyperparameters of the One Class SVM is to split
the data set in training and test sets, train the One Class SVM with the
training set and a pre-specified nu, and see if you get a similar amount of
proportion of outliers (a number close to nu) on the test set. I think that
this is what they mean in Libsvm.
But sometimes you can get the same amount of outliers with the training and
test sets but the set returned by the One Class SVM is not the set you are
looking for.
Albert
Le vendredi 5 septembre 2014, Pagliari, Roberto
<[email protected]<mailto:[email protected]>> a écrit :
I guess my question was mostly about how gridsearch works with oneclass SVM
since once-class SVM does not take into account for labels
Thank you,
From: Pagliari, Roberto
[mailto:[email protected]<javascript:_e(%7B%7D,'cvml','[email protected]');>]
Sent: Friday, September 05, 2014 1:36 PM
To:
[email protected]<javascript:_e(%7B%7D,'cvml','[email protected]');>
Subject: [Scikit-learn-general] one-class SVM with limited number of samples
I am trying to train the one-class SVM.
According to libSVM
Q: How do I choose parameters for one-class SVM as training data are in only
one class?
You have pre-specified true positive rate in mind and then search for
parameters which achieve similar cross-validation accuracy.
But the one-class SVM does not take the labels as an input, as it only works on
X_train.
1. So how should cross-validation be performed, assuming the number of
samples with label ‘1’ is extremely limited.
2. And what if there is none?
Thanks,
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