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Fix doc for apply method. (#6796)

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Jiaming Yuan 2021-03-31 15:28:31 +08:00 committed by GitHub
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1 changed files with 8 additions and 5 deletions
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python-package/xgboost/sklearn.py
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@ -786,7 +786,7 @@ class XGBModel(XGBModelBase):
Parameters Parameters
---------- ----------
X : array_like X : array_like
Data to predict with Data to predict with.
output_margin : bool output_margin : bool
Whether to output the raw untransformed margin value. Whether to output the raw untransformed margin value.
ntree_limit : int ntree_limit : int
@ -798,8 +798,8 @@ class XGBModel(XGBModelBase):
Margin added to prediction. Margin added to prediction.
iteration_range : iteration_range :
Specifies which layer of trees are used in prediction. For example, if a Specifies which layer of trees are used in prediction. For example, if a
random forest is trained with 100 rounds. Specifying `iteration_range=(10, random forest is trained with 100 rounds. Specifying ``iteration_range=(10,
20)`, then only the forests built during [10, 20) (half open set) rounds are 20)``, then only the forests built during [10, 20) (half open set) rounds are
used in this prediction. used in this prediction.
.. versionadded:: 1.4.0 .. versionadded:: 1.4.0
@ -849,8 +849,11 @@ class XGBModel(XGBModelBase):
X : array_like, shape=[n_samples, n_features] X : array_like, shape=[n_samples, n_features]
Input features matrix. Input features matrix.
ntree_limit : int iteration_range :
Limit number of trees in the prediction; defaults to 0 (use all trees). See :py:meth:`xgboost.XGBRegressor.predict`.
ntree_limit :
Deprecated, use ``iteration_range`` instead.
Returns Returns
------- -------