Actually, I found the answer. Both seem to be optimising the loss function for
the various algorithms, below I include some links.
If, we pass class_weight and sample_weight, then the final cost / weight is a
combination of both.
I have a follow up question: in which scenario would we use both? why do some
estimators allow to pass weights both as a dict in the init or as sample
weights in fit? what's the logic? I found it a bit confusing at the beginning.
Thank you!
https://stackoverflow.com/questions/30805192/scikit-learn-random-forest-class-weight-and-sample-weight-parameters
https://stackoverflow.com/questions/30972029/how-does-the-class-weight-parameter-in-scikit-learn-work/30982811#30982811
Soledad Galli
https://www.trainindata.com/
‐‐‐‐‐‐‐ Original Message ‐‐‐‐‐‐‐
On Thursday, December 3, 2020 11:55 AM, Sole Galli via scikit-learn
<scikit-learn@python.org> wrote:
> Hello team,
>
> What is the difference in the implementation of class_weight and
> sample_weight in those algorithms that support both? like random forest or
> logistic regression?
>
> Are both modifying the loss function? in a similar way?
>
> Thank you!
>
> Sole
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