.. _`k-Nearest Neighbors Classifier`:

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

k-Nearest Neighbors Classifier
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.. image:: knn.svg
   :width: 48


Classifier based on the k-nearest neighbors algorithm





Definition
::::::::::


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




Configuration
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    **Algorithm** (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 (for ball_tree or kd_tree)** (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** (metric)
        Metric to use for distance computation. Default is "minkowski", which
        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.
    **Number of jobs** (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.
    **Number of neighbors** (n_neighbors)
        Number of neighbors to use by default for :meth:`kneighbors` queries.
    **Power parameter for the Minkowski metric** (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. This parameter is expected
        to be positive.
    **Weights** (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.

        Refer to the example entitled
        sphx_glr_auto_examples_neighbors_plot_classification.py
        showing the impact of the `weights` parameter on the decision
        boundary.









Implementation
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.. automodule:: node_knn
    :noindex:

.. class:: KNeighborsClassifier
    :noindex: