18697
18203
sklearn.neighbors._classification.KNeighborsClassifier
sklearn.KNeighborsClassifier
sklearn.neighbors._classification.KNeighborsClassifier
5
openml==0.10.2,sklearn==0.23.2
Classifier implementing the k-nearest neighbors vote.
2020-10-11T03:54:35
English
sklearn==0.23.2
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
str or callable
"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
null
Additional keyword arguments for the metric function
n_jobs
int
null
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.
n_neighbors
int
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
No...
p
int
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
"uniform"
openml-python
python
scikit-learn
sklearn
sklearn_0.23.2