| 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 the DistanceMetric class for a list of available metrics | 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 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 | default: 1 |
| n_neighbors | Number of neighbors to use by default for :meth:`k_neighbors` queries | 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 | 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 | default: "uniform" |