t.
    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
    for more details.
    Doesn't affect :meth:`fit` method.

Attributes
----------
effective_metric_ : str or callable
    The distance metric to use. It will be same as the `metric` parameter
    or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
    'minkowski' and `p` parameter set to 2.

effective_metric_params_ : dict
    Additional keyword arguments for the metric function. For most metrics
    will be same with `metric_params` parameter, but may also contain the
    `p` parameter value if the `effective_metric_` attribute is set to
    'minkowski'.

n_features_in_ : int
    Number of features seen during :term:`fit`.

    .. versionadded:: 0.24

feature_names_in_ : ndarray of shape (`n_features_in_`,)
    Names of features seen during :term:`fit`. Defined only when `X`
    has feature names that are all strings.

    .. versionadded:: 1.0

n_samples_fit_ : int
    Number of samples in the fitted data.

See Also
--------
NearestNeighbors : Unsupervised learner for implementing neighbor searches.
RadiusNeighborsRegressor : Regression based on neighbors within a fixed radius.
KNeighborsClassifier : Classifier implementing the k-nearest neighbors vote.
RadiusNeighborsClassifier : Classifier implementing
    a vote among neighbors within a given radius.

Notes
-----
See :ref:`Nearest Neighbors <neighbors>` in the online documentation
for a discussion of the choice of ``algorithm`` and ``leaf_size``.

.. warning::

   Regarding the Nearest Neighbors algorithms, if it is found that two
   neighbors, neighbor `k+1` and `k`, have identical distances but
   different labels, the results will depend on the ordering of the
   training data.

https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm

Examples
--------
>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from sklearn.neighbors import KNeighborsRegressor
>>> neigh = KNeighborsRegressor(n_neighbors=2)
>>> neigh.fit(X, y)
KNeighborsRegressor(...)
>>> print(neigh.predict([[1.5]]))
[0.5]
