Comments (6)
I'll tackle this one if no one else is on it yet.
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Awesome, let me know if you have any questions along the way!
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I want to try this, but i wonder what should i do when i meet a never seen category?
Encoder it as the mean of y in whole train data or something else?
from category_encoders.
@hbghhy awesome! In one-hot, you'll see an example of how we've handled this in the past. There we have options of raising exceptions, ignoring the samples, or imputing something to indicate 'unknown'. For this, imputing the mean of whole training data may be a good approach for 'unknown'.
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@wdm0006 ,I just pull a new request about it. You can see is there something wrong.
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Fixed!
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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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