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Recommend pickling as the way to save XGBClassifier / XGBRegressor / XGBRanker (#3829)

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The `save_model()` and `load_model()` method only saves the part of the model
that's common to all language interfaces and do not preserve Python-specific
attributes, such as `feature_names`. More crucially, label encoder is not
preserved either; this is needed for the scikit-learn wrapper, since you may
have string labels.

Fix: Explicitly recommend pickling as the way to save scikit-learn model
objects.
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Philip Hyunsu Cho 2018-10-25 11:12:41 -07:00 committed by GitHub
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commit d83c818000
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python-package/xgboost/sklearn.py
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@ -241,6 +241,13 @@ class XGBModel(XGBModelBase):
""" """
Save the model to a file. Save the model to a file.
The model is saved in an XGBoost internal binary format which is
universal among the various XGBoost interfaces. Auxiliary attributes of
the Python Booster object (such as feature names) will not be loaded.
Label encodings (text labels to numeric labels) will be also lost.
**If you are using only the Python interface, we recommend pickling the
model object for best results.**
Parameters Parameters
---------- ----------
fname : string fname : string
@ -252,6 +259,13 @@ class XGBModel(XGBModelBase):
""" """
Load the model from a file. Load the model from a file.
The model is loaded from an XGBoost internal binary format which is
universal among the various XGBoost interfaces. Auxiliary attributes of
the Python Booster object (such as feature names) will not be loaded.
Label encodings (text labels to numeric labels) will be also lost.
**If you are using only the Python interface, we recommend pickling the
model object for best results.**
Parameters Parameters
---------- ----------
fname : string or a memory buffer fname : string or a memory buffer