k-Nearest Neighbors Classifier

../../../../_images/knn.svg

Classifier based on the k-nearest neighbors algorithm

Definition

Output ports

out-model model

Output model

Configuration

Algorithm (algorithm)

Algorithm used to compute the nearest neighbors:

  • ‘ball_tree’ will use BallTree

  • ‘kd_tree’ will use KDTree

  • ‘brute’ will use a brute-force search.

  • ‘auto’ will attempt to decide the most appropriate algorithm based on the values passed to fit() method.

Note: fitting on sparse input will override the setting of this parameter, using brute force.

Leaf size (for ball_tree or kd_tree) (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.

Metric (metric)

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 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 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.

Number of jobs (n_jobs)

The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See n_jobs for more details. Doesn’t affect fit() method.

Number of neighbors (n_neighbors)

Number of neighbors to use by default for kneighbors() queries.

Power parameter for the Minkowski metric (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.

Weights (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.

Refer to the example entitled sphx_glr_auto_examples_neighbors_plot_classification.py showing the impact of the weights parameter on the decision boundary.

Implementation

class node_knn.KNeighborsClassifier[source]