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