# How should I sample data from my dataset to be manually labeled for a SVM?

Assume I have a $2$-dimensional dataset $X=(x_1, x_2)$ where both features are not uniformly distributed over their respective ranges.

I now need to select $100$ datapoints from this dataset to be manually labeled, which will be used to train a 2-class SVM.

Should I choose these datapoints randomly or s.t. $x_1$ and $x_2$ are uniformly distributed?

Intuitively the latter makes more sense and should give a more accurate model, right? Unfortunately I can't find any source for that, even though I think it should be a common question. Probably just not googling right.

• I don't think this answers my question. What I meant was that, if my dataset contains $x_1=1$ 3 times as often as $x_1=2$, should my sample contain $x_1=1$ as often as $x_1=2$ (uniformly sampled over the value range) or should I sample randomly (in which case my sample would also approximately contain $x_1=1$ 3 times as often as $x_1=2$). – Claas M. Sep 1 '18 at 17:03