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[doc] Small improvements for categorical data document. (#7330)

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This commit is contained in:
Jiaming Yuan
2021-10-20 18:04:32 +08:00
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parent f999897615
commit 15685996fc
3 changed files with 19 additions and 16 deletions
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doc/tutorials/categorical.rst
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@@ -58,10 +58,12 @@ can plot the model and calculate the global feature importance:
The ``scikit-learn`` interface from dask is similar to single node version. The basic
idea is create dataframe with category feature type, and tell XGBoost to use ``gpu_hist``
with parameter ``enable_categorical``. See `this demo
<https://github.com/dmlc/xgboost/blob/master/demo/guide-python/categorical.py>`_ for a
worked example using categorical data with ``scikit-learn`` interface. For using it with
the Kaggle tutorial dataset, see `this demo
<https://github.com/dmlc/xgboost/blob/master/demo/guide-python/cat_in_the_dat.py>`_
<https://github.com/dmlc/xgboost/blob/master/demo/guide-python/categorical.py>`__ for a
worked example of using categorical data with ``scikit-learn`` interface. A comparison
between using one-hot encoded data and XGBoost's categorical data support can be found
`here
<https://github.com/dmlc/xgboost/blob/master/demo/guide-python/cat_in_the_dat.py>`__.
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@@ -70,10 +72,10 @@ 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 ``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 ``DMatrix`` and the ``train`` function. For
dataframe input:
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:
.. code:: python
@@ -106,7 +108,7 @@ 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
``dask.Array`` can also be used as categorical data.
:class:`dask.Array <dask.Array>` can also be used as categorical data.
**********