[scikit-learn] Are sample weights normalized?

[Michael Eickenberg]

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 at 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
>
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