8000 Fixes #11996 : Handle missing values in OneHotEncoder by Olamyy · Pull Request #12017 · scikit-learn/scikit-learn · GitHub
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Fixes #11996 : Handle missing values in OneHotEncoder #12017

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Fixes #11996 : Handle missing values in OneHotEncoder #12017

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0d3abb1
Initial Logic Implementation
Sep 5, 2018
739e515
Fixing merge conflicts
Sep 5, 2018
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10 changes: 10 additions & 0 deletions doc/modules/preprocessing.rst
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Expand Up @@ -540,6 +540,16 @@ columns for this feature will be all zeros
array([[1., 0., 0., 0., 0., 0.]])


Missing categorical features in the training data can be handled by specifying what happens to them using the ``handle_missing`` parameter. The values for this can be one of :

`all-missing`: This will replace all missing rows with NaN.
`all-zero` : This will replace all mis 6146 sing rows with zeros.
`categorical` : This will replace all missing rows as a representation of a separate one hot column.

Note that, for scikit-learn to handle your missing values using OneHotEncoder, you have to pass a placeholder of what should be recorded as a missing value. This is the `missing_values` parameter and possible values can be either a `NaN` or a custom value of your choice.



See :ref:`dict_feature_extraction` for categorical features that are represented
as a dict, not as scalars.

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