\n",
" \n",
" \n",
" | \n",
" \n",
" \n",
" n_neighbors\n",
" n_neighbors: int, default=5
Number of neighbors to use by default for :meth:`kneighbors` queries.\n",
" \n",
" | \n",
" 5 | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" weights\n",
" weights: {'uniform', 'distance'}, callable or None, 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.
Refer to the example entitled
:ref:`sphx_glr_auto_examples_neighbors_plot_classification.py`
showing the impact of the `weights` parameter on the decision
boundary.\n",
" \n",
" | \n",
" 'uniform' | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" algorithm\n",
" 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.\n",
" \n",
" | \n",
" 'auto' | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" leaf_size\n",
" 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.\n",
" \n",
" | \n",
" 30 | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" p\n",
" p: float, 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. This parameter is expected
to be positive.\n",
" \n",
" | \n",
" 2 | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" metric\n",
" metric: str or callable, default='minkowski'
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
`_ 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.\n",
" \n",
" | \n",
" 'minkowski' | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" metric_params\n",
" metric_params: dict, default=None
Additional keyword arguments for the metric function.\n",
" \n",
" | \n",
" None | \n",
"
\n",
" \n",
"\n",
" \n",
" | \n",
" \n",
" \n",
" n_jobs\n",
" 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 `
for more details.
Doesn't affect :meth:`fit` method.\n",
" \n",
" | \n",
" None | \n",
"
\n",
" \n",
" \n",
"