[MRG] DOC improve glossary entry on categorical features
@@ -143,19 +143,22 @@ General Concepts
as software regressions.
categorical feature
- A categorical or nominal :term:`feature` is one that has a finite set
- of discrete values across the population of data. These are commonly
- represented as columns of integers or strings. Strings will be rejected
- by most scikit-learn estimators, and integers will be treated as
- ordinal or count-valued. For the use with most estimators, categorical
- variables should be one-hot encoded.
- :class:`~sklearn.preprocessing.CategoricalEncoder` helps encoding
- string-valued categorical features. Some estimators may handle
- categorical features better when one-hot encoded. See also
- :ref:`preprocessing_categorical_features` and the
+ A categorical or nominal :term:`feature` is one that has a
+ finite set of discrete values across the population of data.
+ These are commonly represented as columns of integers or
+ strings. Strings will be rejected by most scikit-learn
+ estimators, and integers will be treated as ordinal or
+ count-valued. For the use with most estimators, categorical
+ variables should be one-hot encoded. Notable exceptions include
+ tree-based models such as random forests and gradient boosting
+ models that often work better and faster with integer-coded
+ categorical variables.
+ :class:`~sklearn.preprocessing.CategoricalEncoder` helps
+ encoding string-valued categorical features.
+ See also :ref:`preprocessing_categorical_features` and the
`http://contrib.scikit-learn.org/categorical-encoding
- <category_encoders>`_ package for tools related to encoding categorical
- features.
+ <category_encoders>`_ package for tools related to encoding
+ categorical features.
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