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Revert "Fix #3485, #3540: Don't use dropout for predicting test sets" (#3563)

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* Revert "Fix #3485, #3540: Don't use dropout for predicting test sets (#3556)"

This reverts commit 44811f2330.

* Document behavior of predict() for DART booster

* Add notice to parameter.rst
This commit is contained in:
Philip Hyunsu Cho
2018-08-08 09:48:55 -07:00
committed by GitHub
parent e3e776bd58
commit 3c72654e3b
9 changed files with 61 additions and 30 deletions
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20
python-package/xgboost/core.py
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@@ -1120,10 +1120,22 @@ class Booster(object):
"""
Predict with data.
NOTE: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call bst.copy() to make copies
of model object and then call predict
.. note:: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call ``bst.copy()`` to make copies
of model object and then call ``predict()``.
.. note:: Using ``predict()`` with DART booster
If the booster object is DART type, ``predict()`` will perform dropouts, i.e. only
some of the trees will be evaluated. This will produce incorrect results if ``data`` is
not the training data. To obtain correct results on test sets, set ``ntree_limit`` to
a nonzero value, e.g.
.. code-block:: python
preds = bst.predict(dtest, ntree_limit=num_round)
Parameters
----------

22
python-package/xgboost/sklearn.py
View File

@@ -563,10 +563,24 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
def predict(self, data, output_margin=False, ntree_limit=None):
"""
Predict with `data`.
NOTE: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call xgb.copy() to make copies
of model object and then call predict
.. note:: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies
of model object and then call ``predict()``.
.. note:: Using ``predict()`` with DART booster
If the booster object is DART type, ``predict()`` will perform dropouts, i.e. only
some of the trees will be evaluated. This will produce incorrect results if ``data`` is
not the training data. To obtain correct results on test sets, set ``ntree_limit`` to
a nonzero value, e.g.
.. code-block:: python
preds = bst.predict(dtest, ntree_limit=num_round)
Parameters
----------
data : DMatrix