[doc] Mention data consistency for categorical features. (#9678)

doc/tutorials/categorical.rst

@@ -94,11 +94,11 @@ Using native interface
 **********************
 
 The ``scikit-learn`` interface is user friendly, but lacks some features that are only
-available in native interface.  For instance users cannot compute SHAP value directly or
-use quantized :class:`DMatrix <xgboost.DMatrix>`.  Also native interface supports data
-types other than dataframe, like ``numpy/cupy array``. To use the native interface with
-categorical data, we need to pass the similar parameter to :class:`DMatrix
-<xgboost.DMatrix>` and the :func:`train <xgboost.train>` function.  For dataframe input:
+available in native interface.  For instance users cannot compute SHAP value directly.
+Also native interface supports more data types. To use the native interface with
+categorical data, we need to pass the similar parameter to :class:`~xgboost.DMatrix` or
+:py:class:`~xgboost.QuantileDMatrix` and the :func:`train <xgboost.train>` function.  For
+dataframe input:
 
 .. code:: python
 
@@ -117,7 +117,6 @@ SHAP value computation:
   # categorical features are listed as "c"
   print(booster.feature_types)
 
-
 For other types of input, like ``numpy array``, we can tell XGBoost about the feature
 types by using the ``feature_types`` parameter in :class:`DMatrix <xgboost.DMatrix>`:
 
@@ -131,7 +130,31 @@ types by using the ``feature_types`` parameter in :class:`DMatrix <xgboost.DMatr
 
 For numerical data, the feature type can be ``"q"`` or ``"float"``, while for categorical
 feature it's specified as ``"c"``.  The Dask module in XGBoost has the same interface so
-:class:`dask.Array <dask.Array>` can also be used for categorical data.
+:class:`dask.Array <dask.Array>` can also be used for categorical data. Lastly, the
+sklearn interface :py:class:`~xgboost.XGBRegressor` has the same parameter.
+
+****************
+Data Consistency
+****************
+
+XGBoost accepts parameters to indicate which feature is considered categorical, either through the ``dtypes`` of a dataframe or through the ``feature_types`` parameter. However, XGBoost by itself doesn't store information on how categories are encoded in the first place. For instance, given an encoding schema that maps music genres to integer codes:
+
+.. code-block:: python
+
+  {"acoustic": 0, "indie": 1, "blues": 2, "country": 3}
+
+XGBoost doesn't know this mapping from the input and hence cannot store it in the model. The mapping usually happens in the users' data engineering pipeline with column transformers like :py:class:`sklearn.preprocessing.OrdinalEncoder`. To make sure correct result from XGBoost, users need to keep the pipeline for transforming data consistent across training and testing data. One should watch out for errors like:
+
+.. code-block:: python
+
+  X_train["genre"] = X_train["genre"].astype("category")
+  reg = xgb.XGBRegressor(enable_categorical=True).fit(X_train, y_train)
+
+  # invalid encoding
+  X_test["genre"] = X_test["genre"].astype("category")
+  reg.predict(X_test)
+
+In the above snippet, training data and test data are encoded separately, resulting in two different encoding schemas and invalid prediction result. See :ref:`sphx_glr_python_examples_cat_pipeline.py` for a worked example using ordinal encoder.
 
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 Miscellaneous