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Document tree method for feature weights. (#6312)

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Jiaming Yuan 2020-10-29 04:42:13 +08:00 committed by GitHub
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7
doc/parameter.rst
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@ -108,9 +108,10 @@ Parameters for Tree Booster
'colsample_bynode':0.5}`` with 64 features will leave 8 features to choose from at
each split.
On Python interface, one can set the ``feature_weights`` for DMatrix to define the
probability of each feature being selected when using column sampling. There's a
similar parameter for ``fit`` method in sklearn interface.
On Python interface, when using ``hist``, ``gpu_hist`` or ``exact`` tree method, one
can set the ``feature_weights`` for DMatrix to define the probability of each feature
being selected when using column sampling. There's a similar parameter for ``fit``
method in sklearn interface.
* ``lambda`` [default=1, alias: ``reg_lambda``]

14
python-package/xgboost/sklearn.py
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@ -499,9 +499,10 @@ class XGBModel(XGBModelBase):
A list of the form [L_1, L_2, ..., L_n], where each L_i is a list of
instance weights on the i-th validation set.
feature_weights: array_like
Weight for each feature, defines the probability of each feature
being selected when colsample is being used. All values must be
greater than 0, otherwise a `ValueError` is thrown.
Weight for each feature, defines the probability of each feature being
selected when colsample is being used. All values must be greater than 0,
otherwise a `ValueError` is thrown. Only available for `hist`, `gpu_hist` and
`exact` tree methods.
callbacks : list of callback functions
List of callback functions that are applied at end of each iteration.
It is possible to use predefined callbacks by using :ref:`callback_api`.
@ -1237,9 +1238,10 @@ class XGBRanker(XGBModel):
file name of stored XGBoost model or 'Booster' instance XGBoost
model to be loaded before training (allows training continuation).
feature_weights: array_like
Weight for each feature, defines the probability of each feature
being selected when colsample is being used. All values must be
greater than 0, otherwise a `ValueError` is thrown.
Weight for each feature, defines the probability of each feature being
selected when colsample is being used. All values must be greater than 0,
otherwise a `ValueError` is thrown. Only available for `hist`, `gpu_hist` and
`exact` tree methods.
callbacks : list of callback functions
List of callback functions that are applied at end of each
iteration. It is possible to use predefined callbacks by using