DOC Linear regression n_jobs doc update (scikit-learn#21258)

simonandras · thomasjpfan · jjerphan · glemaitre · commit d962049bb70b · 2021-10-23T12:01:06.000+02:00
* update docstring

* Apply suggestions from code review

Co-authored-by: Thomas J. Fan &lt;thomasjpfan@gmail.com&gt;

* Update sklearn/linear_model/_base.py

Co-authored-by: Julien Jerphanion &lt;git@jjerphan.xyz&gt;

Co-authored-by: Thomas J. Fan &lt;thomasjpfan@gmail.com&gt;
Co-authored-by: Julien Jerphanion &lt;git@jjerphan.xyz&gt;
diff --git a/sklearn/linear_model/_base.py b/sklearn/linear_model/_base.py
@@ -536,10 +536,11 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
 
     n_jobs : int, default=None
         The number of jobs to use for the computation. This will only provide
-        speedup for n_targets > 1 and sufficient large problems.
-        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
-        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
-        for more details.
+        speedup in case of sufficiently large problems, that is if firstly
+        `n_targets > 1` and secondly `X` is sparse or if `positive` is set
+        to `True`. ``None`` means 1 unless in a
+        :obj:`joblib.parallel_backend` context. ``-1`` means using all
+        processors. See :term:`Glossary <n_jobs>` for more details.
 
     positive : bool, default=False
         When set to ``True``, forces the coefficients to be positive. This
