KNeighborsClassifier()

Posted 2021-07-31 yangbocsu

Init signature:
KNeighborsClassifier(
    n_neighbors=5,
    *,
    weights='uniform',
    algorithm='auto',
    leaf_size=30,
    p=2,
    metric='minkowski',
    metric_params=None,
    n_jobs=None,
    **kwargs,
)
Docstring:     
Classifier implementing the k-nearest neighbors vote.

Read more in the :ref:`User Guide <classification>`.

Parameters
----------
n_neighbors : int, default=5
    Number of neighbors to use by default for :meth:`kneighbors` queries.

weights : {'uniform', 'distance'} or callable, default='uniform'
    weight function used in prediction.  Possible values:

    - 'uniform' : uniform weights.  All points in each neighborhood
      are weighted equally.
    - 'distance' : weight points by the inverse of their distance.
      in this case, closer neighbors of a query point will have a
      greater influence than neighbors which are further away.
    - [callable] : a user-defined function which accepts an
      array of distances, and returns an array of the same shape
      containing the weights.

algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
    Algorithm used to compute the nearest neighbors:

    - 'ball_tree' will use :class:`BallTree`
    - 'kd_tree' will use :class:`KDTree`
    - 'brute' will use a brute-force search.
    - 'auto' will attempt to decide the most appropriate algorithm
      based on the values passed to :meth:`fit` method.

    Note: fitting on sparse input will override the setting of
    this parameter, using brute force.

leaf_size : int, default=30
    Leaf size passed to BallTree or KDTree.  This can affect the
    speed of the construction and query, as well as the memory
    required to store the tree.  The optimal value depends on the
    nature of the problem.

p : int, default=2
    Power parameter for the Minkowski metric. When p = 1, this is
    equivalent to using manhattan_distance (l1), and euclidean_distance
    (l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used.

metric : str or callable, default='minkowski'
    the distance metric to use for the tree.  The default metric is
    minkowski, and with p=2 is equivalent to the standard Euclidean
    metric. See the documentation of :class:`DistanceMetric` for a
    list of available metrics.
    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.

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.
    Doesn't affect :meth:`fit` method.

Attributes
----------
classes_ : array of shape (n_classes,)
    Class labels known to the classifier

effective_metric_ : str or callble
    The distance metric used. 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_samples_fit_ : int
    Number of samples in the fitted data.

outputs_2d_ : bool
    False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
    otherwise True.

Examples
--------
>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from sklearn.neighbors import KNeighborsClassifier
>>> neigh = KNeighborsClassifier(n_neighbors=3)
>>> neigh.fit(X, y)
KNeighborsClassifier(...)
>>> print(neigh.predict([[1.1]]))
[0]
>>> print(neigh.predict_proba([[0.9]]))
[[0.66666667 0.33333333]]

See Also
--------
RadiusNeighborsClassifier
KNeighborsRegressor
RadiusNeighborsRegressor
NearestNeighbors

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_neighbor_algorithm
File:           d:\\programdata\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py
Type:           ABCMeta
Subclasses: