| Issue | #Downvotes for this reason | By |
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| algorithm | default: "auto" | |
| 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 | default: 30 |
| metric | 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 | default: "minkowski" |
| metric_params | Additional keyword arguments for the metric function | default: null |
| 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 :term:`Glossary | default: null |
| n_neighbors | 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... | default: 5 |
| 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 | default: 2 |
| weights | default: "uniform" |