[testing] check results are coherent vs sklearn when using sample weights

[tlienart]

We've recently added (https://github.com/alan-turing-institute/MLJModels.jl/pull/125) the possibility to add weights to samples in KNNC, KNNR. It seems fine but it would still be good to check this a bit more and ideally against an external benchmark like Sklearn which I believe supports sample weights as well.

Steps:

• ~~be on the dev branch of MLJModels~~ edit This now lives at NearestNeighborModels (current repo)
• generate some dummy data with dummy weights (see also examples in tests for NearestNeighbors though it'd be better to use less dumb data where classes overlap a bit)
• save the data and do the same analysis in sklearn
• check that the results look roughly similar (like accuracy within +- 5%)