Add `sample_weights` option to utility.

[kosmitive]

While it is clear for models which have a class_weights parameter in the constructor, some models only support setting sample_weights in fit. We have two options

1. Add mechanism to utility to pass parameters to the fit method.
2. Wrap the model, e.g. proxy all methods and when fit is called, add sample_weights

to accept imbalanced datasets. Which method would you prefer?

I would tend for 2., because the interface fit is more compact and reusable.

[mdbenito]

I would say that balancing class imbalance is the responsibility of the user, and therefore so is wrapping the model to do whatever makes sense for it. I don't think pydvl needs to provide any kind of support for this. I would agree to using imbalanced data in an example to illustrate the problem, thouh. Or do you think there is some non-trivial situation that we should handle?

[github-actions[bot]]

Stale issue: awaiting OPs reply for 30 days

[github-actions[bot]]

This issue was closed because it has been stalled for 7 days with no activity.