results in the standard Euclidean distance when p = 2. See the
    documentation of `scipy.spatial.distance
    <https://docs.scipy.org/doc/scipy/reference/spatial.distance.html>`_ and
    the metrics listed in
    :class:`~sklearn.metrics.pairwise.distance_metrics` for valid metric
    values.

    If metric is "precomputed", X is assumed to be a distance matrix and
    must be square during fit. X may be a :term:`sparse graph`, in which
    case only "nonzero" elements may be considered neighbors.

    If metric is a callable function, it takes two arrays representing 1D
    vectors as inputs and must return one value indicating the distance
    between those vectors. This works for Scipy's metrics, but is less
    efficient than passing the metric name as a string.

metric_params : dict, default=None
    Additional keyword arguments for the metric function.

n_jobs : int, default=None
    The number of parallel jobs to run for neighbors search.
    ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
    ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
    for more details.

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.
KNeighborsRegressor : Regression based on k-nearest neighbors.
KNeighborsClassifier : Classifier based on the k-nearest neighbors.
RadiusNeighborsClassifier : Classifier based on 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``.

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

Examples
--------
>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from sklearn.neighbors import RadiusNeighborsRegressor
>>> neigh = RadiusNeighborsRegressor(radius=1.0)
>>> neigh.fit(X, y)
RadiusNeighborsRegressor(...)
>>> print(neigh.predict([[1.5]]))
[0.5]
r