DOC Minor update to random state glossary
@@ -1541,16 +1541,20 @@ functions or non-estimator constructors.
mean that randomization is always used, as it may be dependent on
another parameter, e.g. ``shuffle``, being set.
- ``random_state``'s value may be:
+ The passed value will have an effect on the reproducibility of the
+ results returned by the function (:term:`fit`, :term:`split`, or any
+ other function like :func:`~sklearn.cluster.k_means`). `random_state`'s
+ value may be:
None (default)
- Use the global random state from :mod:`numpy.random`.
+ Use the global random state instance from :mod:`numpy.random`.
+ Calling the function multiple times will reuse
+ the same instance, and will produce different results.
An integer
Use a new random number generator seeded by the given integer.
- To make a randomized algorithm deterministic (i.e. running it
- multiple times will produce the same result), an arbitrary
- integer ``random_state`` can be used. However, it may be
+ Using an int will produce the same results across different calls.
+ However, it may be
worthwhile checking that your results are stable across a
number of different distinct random seeds. Popular integer
random seeds are 0 and `42
@@ -1558,9 +1562,9 @@ functions or non-estimator constructors.
A :class:`numpy.random.RandomState` instance
Use the provided random state, only affecting other users
- of the same random state instance. Calling fit multiple times
- will reuse the same instance, and will produce different
- results.
+ of that same random state instance. Calling the function
+ multiple times will reuse the same instance, and
+ will produce different results.
:func:`utils.check_random_state` is used internally to validate the
input ``random_state`` and return a :class:`~numpy.random.RandomState`
