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Is there any plan or demand for supporting sample_weight option in the fit method of estimators?
Since a number of model classes in scikit-learn support it, I suppose it would be useful for estimators such as StackingRegressor (provided that all regressors accept it).
I saw a related PR #255 about the support for fit_params options. Once this is merged, sample_weight will be implementable by passing same weights to all regressors and meta-regressor. Even so, API like fit(X, y [, sample_weight]) can be handy.
The text was updated successfully, but these errors were encountered:
kota7
changed the title
Support for sample_weight in the fit method.
Support for sample_weight in the fit method
Sep 23, 2018
I think support for sample_weight would be a good thing to have. I think if the fit methods support fit_params the sample_weight would be supported automatically, right? I am currently not sure whether #255 is still active. If not, would you like to take it over?
@rasbt I will have a look. At first glance, it looks close but not entirely automatic. In #255 it looks fit_params are given per estimator basis, while support for sample_weight requires options applied to all estimators in the both levels.
Is there any plan or demand for supporting
sample_weight
option in thefit
method of estimators?Since a number of model classes in scikit-learn support it, I suppose it would be useful for estimators such as
StackingRegressor
(provided that all regressors accept it).I saw a related PR #255 about the support for
fit_params
options. Once this is merged,sample_weight
will be implementable by passing same weights to all regressors and meta-regressor. Even so, API likefit(X, y [, sample_weight])
can be handy.The text was updated successfully, but these errors were encountered: