Hi Michael, thanks for the response. Based on what you said, is it correct to assume that weights are relative to the size of the data set? Eg
If my dataset size is 200 and I have 1 of sample 1, 10 of sample 2 and 100 of sample 3, sample 3 will be given a lot of focus during training because it exists in majority, but if my dataset size was say 1 million, these weights wouldn't really affect much? Thanks, Abhishek On Jul 28, 2017 10:41 PM, "Michael Eickenberg" <michael.eickenb...@gmail.com> wrote: > Hi Abhishek, > > think of your example as being equivalent to putting 1 of sample 1, 10 of > sample 2 and 100 of sample 3 in a dataset and then run your SVM. > This is exactly true for some estimators and approximately true for > others, but always a good intuition. > > Hope this helps! > Michael > > > On Fri, Jul 28, 2017 at 10:01 AM, Abhishek Raj via scikit-learn < > scikit-learn@python.org> wrote: > >> Hi, >> >> I am using one class svm for binary classification and was just curious >> what is the range/scale for sample weights? Are they normalized internally? >> For example - >> >> Sample 1, weight - 1 >> Sample 2, weight - 10 >> Sample 3, weight - 100 >> >> Does this mean Sample 3 will always be predicted as positive and sample 1 >> will never be predicted as positive? What about sample 2? >> >> Also, what would happen if I assign a high weight to majority of the >> samples and low weights to the rest. Eg if 80% of my samples were weighted >> 1000 and 20% were weighted 1. >> >> A clarification or a link to read up on how exactly weights affect the >> training process would be really helpful. >> >> 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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