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sklearn.neighbors._classification.KNeighborsClassifier

sklearn.neighbors._classification.KNeighborsClassifier

Visibility: public Uploaded 17-10-2024 by Test Test sklearn==1.5.2 numpy>=1.19.5 scipy>=1.6.0 joblib>=1.2.0 threadpoolctl>=3.1.0 7 runs
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  • openml-python python scikit-learn sklearn sklearn_1.5.2
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Classifier implementing the k-nearest neighbors vote.

Parameters

algorithmdefault: "auto"
leaf_sizeLeaf 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 problemdefault: 30
metricMetric 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 stringdefault: "minkowski"
metric_paramsAdditional keyword arguments for the metric functiondefault: null
n_jobsThe 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.default: null
n_neighborsNumber of neighbors to use by default for :meth:`kneighbors` queries 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 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 :c...default: 5
pPower 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 positivedefault: 2
weightsdefault: "uniform"

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