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The current implementation of TargetEncoder seems to calculate (shrinked) averages of y. In cases with sample_weights, it would be more natural to work with (shrinked) weighted averages.
Describe your proposed solution
In case of sample_weights, shrinked averages should be replaced by corresponding shrinked weighted averages.
However, I am not 100% sure if sample_weights are accessable by a transformer.
Describe alternatives you've considered, if relevant
The alternative is to continue ignoring sample weights.
Additional context
No response
The text was updated successfully, but these errors were encountered:
In principle sample_weight can be accessed when fitting TargetEncoder. So at first sight this is something that can be considered as a new feature. There seems to be some support for it as well (👍 on the issue).
Describe the workflow you want to enable
The current implementation of
TargetEncoder
seems to calculate (shrinked) averages ofy
. In cases withsample_weights
, it would be more natural to work with (shrinked) weighted averages.Describe your proposed solution
In case of
sample_weights
, shrinked averages should be replaced by corresponding shrinked weighted averages.However, I am not 100% sure if
sample_weights
are accessable by a transformer.Describe alternatives you've considered, if relevant
The alternative is to continue ignoring sample weights.
Additional context
No response
The text was updated successfully, but these errors were encountered: