KNeighborsClassifier?
Init signature:
KNeighborsClassifier(
n_neighbors=5,
*,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None,
n_jobs=None,
)
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. For a list of available metrics, see the documentation of
:class:`~sklearn.metrics.DistanceMetric`.
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_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.
outputs_2d_ : bool
False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
otherwise True.
See Also
--------
RadiusNeighborsClassifier: Classifier based on neighbors within a fixed radius.
KNeighborsRegressor: Regression based on k-nearest neighbors.
RadiusNeighborsRegressor: Regression based on neighbors within a fixed radius.
NearestNeighbors: Unsupervised learner for implementing neighbor searches.
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
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]]))
[[0.666... 0.333...]]
File: /Applications/anaconda3/lib/python3.9/site-packages/sklearn/neighbors/_classification.py
Type: ABCMeta
Subclasses: