Basically passing class weights should be equivalent to passing
per-class-constant sample weights.
> 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?
SW is a per-sample property (aligned with X and y) so we avoid passing
those to init because the data isn't known when initializing the
estimator. It's only known when calling fit. In general we avoid passing
data-related info into init so that the same instance can be fitted on
any data (with different number of samples, different classes, etc.).
We allow to pass class_weight in init because the 'balanced' option is
data-agnostic. Arguably, allowing a dict with actual class values
violates the above argument (of not having data-related stuff in init),
so I guess that's where the logic ends ;)
As to why one would use both, I'm not so sure honestly.
Nicolas
On 12/4/20 10:40 AM, Sole Galli via scikit-learn wrote:
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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