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Validate out of range categorical value. (#7576)

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* Use float in CPU categorical set to preserve the input value.
* Check out of range values.
This commit is contained in:
Jiaming Yuan
2022-01-18 20:16:19 +08:00
committed by GitHub
parent d6ea5cc1ed
commit deab0e32ba
8 changed files with 86 additions and 38 deletions
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doc/tutorials/categorical.rst
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@@ -114,11 +114,11 @@ Miscellaneous
By default, XGBoost assumes input categories are integers starting from 0 till the number
of categories :math:`[0, n_categories)`. However, user might provide inputs with invalid
values due to mistakes or missing values. It can be negative value, floating point value
that can not be represented by 32-bit integer, or values that are larger than actual
number of unique categories. During training this is validated but for prediction it's
treated as the same as missing value for performance reasons. Lastly, missing values are
treated as the same as numerical features.
values due to mistakes or missing values. It can be negative value, integer values that
can not be accurately represented by 32-bit floating point, or values that are larger than
actual number of unique categories. During training this is validated but for prediction
it's treated as the same as missing value for performance reasons. Lastly, missing values
are treated as the same as numerical features (using the learned split direction).
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