Category Encoders v1.2.5 Release

This post was originally published here

This release was actually cut a couple of weeks ago, but I forgot to put a post here. It's been a release of mainly incremental changes, but also one of increased contributions from the community, so while not a huge feature-packed release, it's one I'm particularly proud of.  Here's to more like this.

It was around 4 months since the last release, which I think is a pretty decent cadence, considering our level of development.

Some highlights:

  • Andrethrill did some work to make the usage of binary encoding more stable when training/transforming on datasets with different counts of categories
  • The same thing got done in BaseNEncoder
  • Cameron Davison updated the type coercion code for Pandas DataFrames was changed to quiet some deprecation warnings.
  • Cameron Davison also did some work to ensure consistent ordering of categories in the ordinal encoder, and the encoders which use it.
  • HBGHHY added leave-one-out encoding, a new method for us, found on Kaggle.

So if you haven't used it already, check out category encoders, it's great. If you do use it and like it, hop on over to github and join us, there's always something new to work on.

https://github.com/scikit-learn-contrib/categorical-encoding

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