18906
6691
sklearn.neighbors.classification.KNeighborsClassifier
sklearn.KNeighborsClassifier
sklearn.neighbors.classification.KNeighborsClassifier
44
openml==0.12.2,sklearn==0.18.1
Classifier implementing the k-nearest neighbors vote.
2021-08-13T19:23:29
English
sklearn==0.18.1
numpy>=1.6.1
scipy>=0.9
algorithm
"auto"
leaf_size
int
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
metric
string or DistanceMetric object
"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 the DistanceMetric class for a
list of available metrics
metric_params
dict
null
Additional keyword arguments for the metric function
n_jobs
int
1
The number of parallel jobs to run for neighbors search
If ``-1``, then the number of jobs is set to the number of CPU cores
Doesn't affect :meth:`fit` method
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) # doctest: +ELLIPSIS
KNeighborsClassifier(...)
>>> print(neigh.predict([[1.1]]))
[0]
>>> print(neigh.predict_proba([[0.9]]))
[[ 0.66666667 0.33333333]]
See also
--------
RadiusNeighborsClassifier
KNeighborsRegressor
RadiusNeighborsRegressor
NearestNeighbors
n_neighbors
int
1
Number of neighbors to use by default for :meth:`k_neighbors` queries
p
integer
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
weights
str or callable
"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'}, optional
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
openml-python
python
scikit-learn
sklearn
sklearn_0.18.1