.. _`k-Nearest Neighbors Classifier`:

.. _`org.sysess.sympathy.machinelearning.knn`:

k-Nearest Neighbors Classifier
==============================

.. image:: knn.svg
   :width: 48


Classifier based on the k-nearest neighbors algorithm



**Documentation**

Classifier based on the k-nearest neighbors algorithm

*Configuration*:


  - *n_neighbors*

    Number of neighbors to use by default for :meth:`kneighbors` queries.


  - *weights*

    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*

    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*

    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.


  - *metric*

    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 the DistanceMetric class 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 sparse graph,
    in which case only "nonzero" elements may be considered neighbors.


  - *p*

    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.


  - *n_jobs*

    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 n_jobs
    for more details.
    Doesn't affect :meth:`fit` method.




*Attributes*:




*Input ports*:


*Output ports*:
    **out-model** : model
        Output model


**Definition**


*Input ports*


*Output ports*

    :out-model:  model

        Output model





.. automodule:: node_knn

.. class:: KNeighborsClassifier