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[doc] Fix typo. [skip ci] (#7311)

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Jiaming Yuan 2021-10-12 19:10:18 +08:00 committed by GitHub
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10
doc/tutorials/categorical.rst
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@ -50,7 +50,7 @@ can plot the model and calculate the global feature importance:
# Get a graph # Get a graph
graph = xgb.to_graphviz(clf, num_trees=1) graph = xgb.to_graphviz(clf, num_trees=1)
# Or get a matplotlib axis # Or get a matplotlib axis
ax = xgb.plot_tree(reg, num_trees=1) ax = xgb.plot_tree(clf, num_trees=1)
# Get feature importances # Get feature importances
clf.feature_importances_ clf.feature_importances_
@ -60,8 +60,8 @@ idea is create dataframe with category feature type, and tell XGBoost to use ``g
with parameter ``enable_categorical``. See `this demo with parameter ``enable_categorical``. See `this demo
<https://github.com/dmlc/xgboost/blob/master/demo/guide-python/categorical.py>`_ for a <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 worked example using categorical data with ``scikit-learn`` interface. For using it with
the Kaggle tutorial dataset, see `<this demo 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/cat_in_the_dat.py>`_
********************** **********************
@ -114,5 +114,5 @@ Next Steps
********** **********
As of XGBoost 1.5, the feature is highly experimental and have limited features like CPU As of XGBoost 1.5, the feature is highly experimental and have limited features like CPU
training is not yet supported. Please see `<this issue> training is not yet supported. Please see `this issue
https://github.com/dmlc/xgboost/issues/6503`_ for progress. <https://github.com/dmlc/xgboost/issues/6503>`_ for progress.