Run 8738

Task 31 (Supervised Classification) mfeat-fourier Uploaded 24-01-2024 by Test Test
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sklearn.neighbors._classification.KNeighborsClassifier(3)	Classifier implementing the k-nearest neighbors vote.
sklearn.neighbors._classification.KNeighborsClassifier(3)_algorithm	"auto"
sklearn.neighbors._classification.KNeighborsClassifier(3)_leaf_size	30
`_ 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">sklearn.neighbors._classification.KNeighborsClassifier(3)_metric	"minkowski"
sklearn.neighbors._classification.KNeighborsClassifier(3)_metric_params	null
sklearn.neighbors._classification.KNeighborsClassifier(3)_n_jobs	null
sklearn.neighbors._classification.KNeighborsClassifier(3)_n_neighbors	5
sklearn.neighbors._classification.KNeighborsClassifier(3)_p	2
sklearn.neighbors._classification.KNeighborsClassifier(3)_weights	"uniform"