Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[doc] Brief introduction to base_score. (#9882)

Browse Source
This commit is contained in:
Jiaming Yuan
2023-12-17 13:34:34 +08:00
committed by GitHub
parent db7f952ed6
commit 0edd600f3d
6 changed files with 114 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
python-package/xgboost/core.py
View File

@@ -785,7 +785,7 @@ class DMatrix: # pylint: disable=too-many-instance-attributes,too-many-public-m
so it doesn't make sense to assign weights to individual data points.
base_margin :
Base margin used for boosting from existing model.
Global bias for each instance. See :doc:`/tutorials/intercept` for details.
missing :
Value in the input data which needs to be present as a missing value. If
None, defaults to np.nan.

8
python-package/xgboost/sklearn.py
View File

@@ -1006,7 +1006,7 @@ class XGBModel(XGBModelBase):
sample_weight :
instance weights
base_margin :
global bias for each instance.
Global bias for each instance. See :doc:`/tutorials/intercept` for details.
eval_set :
A list of (X, y) tuple pairs to use as validation sets, for which
metrics will be computed.
@@ -1146,7 +1146,7 @@ class XGBModel(XGBModelBase):
When this is True, validate that the Booster's and data's feature_names are
identical. Otherwise, it is assumed that the feature_names are the same.
base_margin :
Margin added to prediction.
Global bias for each instance. See :doc:`/tutorials/intercept` for details.
iteration_range :
Specifies which layer of trees are used in prediction. For example, if a
random forest is trained with 100 rounds. Specifying ``iteration_range=(10,
@@ -1599,7 +1599,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
When this is True, validate that the Booster's and data's feature_names are
identical. Otherwise, it is assumed that the feature_names are the same.
base_margin :
Margin added to prediction.
Global bias for each instance. See :doc:`/tutorials/intercept` for details.
iteration_range :
Specifies which layer of trees are used in prediction. For example, if a
random forest is trained with 100 rounds. Specifying `iteration_range=(10,
@@ -1942,7 +1942,7 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
weights to individual data points.
base_margin :
Global bias for each instance.
Global bias for each instance. See :doc:`/tutorials/intercept` for details.
eval_set :
A list of (X, y) tuple pairs to use as validation sets, for which
metrics will be computed.