DOC on parallelisation of custom scorer
@@ -215,6 +215,25 @@ the following two rules:
Again, by convention higher numbers are better, so if your scorer
returns loss, that value should be negated.
+.. note:: **Using custom scorers in functions where n_jobs > 1**
+
+ While defining the custom scoring function alongside the calling function
+ should work out of the box with the default joblib backend (loky),
+ importing it from another module will be a more robust approach and work
+ independently of the joblib backend.
+
+ For example, to use, ``n_jobs`` greater than 1 in the example below,
+ ``custom_scoring_function`` function is saved in a user-created module
+ (``custom_scorer_module.py``) and imported::
+
+ >>> from custom_scorer_module import custom_scoring_function # doctest: +SKIP
+ >>> cross_val_score(model,
+ ... X_train,
+ ... y_train,
+ ... scoring=make_scorer(custom_scoring_function, greater_is_better=False),
+ ... cv=5,
+ ... n_jobs=-1) # doctest: +SKIP
+
.. _multimetric_scoring:
Using multiple metric evaluation
