Comments (2)
wdm0006
commented on July 24, 2024
Hmm, it might run, but I'm not sure that it will give you results that work out as you'd like. Worth a shot, if it does work it's not intentional but comment back here, it'd be cool.
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mrgloom
commented on July 24, 2024
Looks like it works without throwing exception, but quality is less then my naive way of mean encoding even without k-fold, smoothing, etc.
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Related Issues (20)
- get_feature_names_out is incompatible with sklearn estimators and eli5, consequently HOT 3
- Equivalent method to sklearn's partial_fit? HOT 1
- CountEncoder incorrectly counts Timestamp columns HOT 3
- Target encoding categories with a single training example HOT 1
- DOC: one of the source links is dead HOT 1
- Missing text in documentation HOT 2
- Support Pandas 2.1 HOT 1
- Feature Request: Count-Based Target Encoder (Dracula)? HOT 1
- Pandas' string columns are not recognized HOT 3
- Pandas copy-on-write doesn't work properly HOT 2
- pd.NA should behave as np.nan HOT 5
- Multidimensional/composite target encoding HOT 4
- FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. HOT 2
- Support for Spark HOT 1
- EOF Error Raised while Calling HashingEncoders function HOT 6
- why we combine this library with main sklearn ? HOT 1
- catboost encoder get different result with catboost HOT 8
- Combining with set_output can produce errors HOT 1
- AttributeError: 'DataFrame' object has no attribute 'unique' HOT 1
- [Question; need help; support request] Possible to join multiple CountEncoders after parallel (multiprocessing) fitting? HOT 1
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