KNeighborsClassifier (2024)

Classifier implementing the k-nearest neighbors vote.

Read more in the User Guide.

Parameters:

n_neighborsint, default=5

Number of neighbors to use by default for kneighbors queries.

weights{‘uniform’, ‘distance’}, callable or None, default=’uniform’

Weight function used in prediction. Possible values:

• ‘uniform’ : uniform weights. All points in each neighborhoodare weighted equally.

• ‘distance’ : weight points by the inverse of their distance.in this case, closer neighbors of a query point will have agreater influence than neighbors which are further away.

• [callable] : a user-defined function which accepts anarray of distances, and returns an array of the same shapecontaining the weights.

Refer to the example entitledNearest Neighbors Classificationshowing the impact of the weights parameter on the decisionboundary.

algorithm{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’

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 algorithmbased on the values passed to fit method.

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

leaf_sizeint, default=30

Leaf size passed to BallTree or KDTree. This can affect thespeed of the construction and query, as well as the memoryrequired to store the tree. The optimal value depends on thenature of the problem.

pfloat, default=2

Power parameter for the Minkowski metric. When p = 1, this is equivalentto using manhattan_distance (l1), and euclidean_distance (l2) for p = 2.For arbitrary p, minkowski_distance (l_p) is used. This parameter is expectedto be positive.

metricstr or callable, default=’minkowski’

Metric to use for distance computation. Default is “minkowski”, whichresults in the standard Euclidean distance when p = 2. See thedocumentation of scipy.spatial.distance andthe metrics listed indistance_metrics for valid metricvalues.

If metric is “precomputed”, X is assumed to be a distance matrix andmust be square during fit. X may be a sparse graph, in whichcase only “nonzero” elements may be considered neighbors.

If metric is a callable function, it takes two arrays representing 1Dvectors as inputs and must return one value indicating the distancebetween those vectors. This works for Scipy’s metrics, but is lessefficient than passing the metric name as a string.

metric_paramsdict, default=None

Additional keyword arguments for the metric function.

n_jobsint, default=None

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 Glossaryfor more details.Doesn’t affect fit method.

Attributes:

classes_array of shape (n_classes,)

Class labels known to the classifier

effective_metric_str or callble

The distance metric used. It will be same as the metric parameteror a synonym of it, e.g. ‘euclidean’ if the metric parameter set to‘minkowski’ and p parameter set to 2.

effective_metric_params_dict

Additional keyword arguments for the metric function. For most metricswill be same with metric_params parameter, but may also contain thep parameter value if the effective_metric_ attribute is set to‘minkowski’.

n_features_in_int

Number of features seen during fit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when Xhas feature names that are all strings.

Added in version 1.0.

n_samples_fit_int

Number of samples in the fitted data.

outputs_2d_bool

False when y’s shape is (n_samples, ) or (n_samples, 1) during fitotherwise True.

Notes

See Nearest Neighbors in the online documentationfor a discussion of the choice of algorithm and leaf_size.

https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm

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)KNeighborsClassifier(...)>>> print(neigh.predict([[1.1]]))[0]>>> print(neigh.predict_proba([[0.9]]))[[0.666... 0.333...]]

fit(X, y)[source]#

Fit the k-nearest neighbors classifier from the training dataset.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric=’precomputed’

Training data.

y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs)

Target values.

Returns:

selfKNeighborsClassifier

The fitted k-nearest neighbors classifier.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routingmechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

kneighbors(X=None, n_neighbors=None, return_distance=True)[source]#

Find the K-neighbors of a point.

Returns indices of and distances to the neighbors of each point.

Parameters:

X{array-like, sparse matrix}, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’, default=None

The query point or points.If not provided, neighbors of each indexed point are returned.In this case, the query point is not considered its own neighbor.

n_neighborsint, default=None

Number of neighbors required for each sample. The default is thevalue passed to the constructor.

return_distancebool, default=True

Whether or not to return the distances.

Returns:

neigh_distndarray of shape (n_queries, n_neighbors)

Array representing the lengths to points, only present ifreturn_distance=True.

neigh_indndarray of shape (n_queries, n_neighbors)

Indices of the nearest points in the population matrix.

Examples

In the following example, we construct a NearestNeighborsclass from an array representing our data set and ask who’sthe closest point to [1,1,1]

>>> samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]>>> from sklearn.neighbors import NearestNeighbors>>> neigh = NearestNeighbors(n_neighbors=1)>>> neigh.fit(samples)NearestNeighbors(n_neighbors=1)>>> print(neigh.kneighbors([[1., 1., 1.]]))(array([[0.5]]), array([[2]]))

As you can see, it returns [[0.5]], and [[2]], which means that theelement is at distance 0.5 and is the third element of samples(indexes start at 0). You can also query for multiple points:

>>> X = [[0., 1., 0.], [1., 0., 1.]]>>> neigh.kneighbors(X, return_distance=False)array([[1], [2]]...)

kneighbors_graph(X=None, n_neighbors=None, mode='connectivity')[source]#

Compute the (weighted) graph of k-Neighbors for points in X.

Parameters:

X{array-like, sparse matrix} of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’, default=None

The query point or points.If not provided, neighbors of each indexed point are returned.In this case, the query point is not considered its own neighbor.For metric='precomputed' the shape should be(n_queries, n_indexed). Otherwise the shape should be(n_queries, n_features).

n_neighborsint, default=None

Number of neighbors for each sample. The default is the valuepassed to the constructor.

mode{‘connectivity’, ‘distance’}, default=’connectivity’

Type of returned matrix: ‘connectivity’ will return theconnectivity matrix with ones and zeros, in ‘distance’ theedges are distances between points, type of distancedepends on the selected metric parameter inNearestNeighbors class.

Returns:

Asparse-matrix of shape (n_queries, n_samples_fit)

n_samples_fit is the number of samples in the fitted data.A[i, j] gives the weight of the edge connecting i to j.The matrix is of CSR format.

See also

NearestNeighbors.radius_neighbors_graph

Compute the (weighted) graph of Neighbors for points in X.

See Also

The k-Nearest Neighbors (kNN) Algorithm in Python – Real Python

Examples

>>> X = [[0], [3], [1]]>>> from sklearn.neighbors import NearestNeighbors>>> neigh = NearestNeighbors(n_neighbors=2)>>> neigh.fit(X)NearestNeighbors(n_neighbors=2)>>> A = neigh.kneighbors_graph(X)>>> A.toarray()array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]])

predict(X)[source]#

Predict the class labels for the provided data.

Parameters:

X{array-like, sparse matrix} of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’

Test samples.

Returns:

yndarray of shape (n_queries,) or (n_queries, n_outputs)

Class labels for each data sample.

predict_proba(X)[source]#

Return probability estimates for the test data X.

Parameters:

X{array-like, sparse matrix} of shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’

Test samples.

Returns:

pndarray of shape (n_queries, n_classes), or a list of n_outputs of such arrays if n_outputs > 1.

The class probabilities of the input samples. Classes are orderedby lexicographic order.

score(X, y, sample_weight=None)[source]#

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracywhich is a harsh metric since you require for each sample thateach label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features)

Test samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects(such as Pipeline). The latter haveparameters of the form <component>__<parameter> so that it’spossible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → KNeighborsClassifier[source]#

Request metadata passed to the score method.

Note that this method is only relevant ifenable_metadata_routing=True (see sklearn.set_config).Please see User Guide on how the routingmechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as asub-estimator of a meta-estimator, e.g. used inside aPipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.