Well, that will depend on how your estimator works. But in general you are right - if you assume that samples 4 to N are weighted with the same weight (e.g. 1) in both cases, then the sample 3 will be relatively less important in the larger training set.
On Fri, Jul 28, 2017 at 1:06 PM, Abhishek Raj via scikit-learn < scikit-learn@python.org> wrote: > 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 >> >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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