sklearn.neighbors.KNeighborsClassifier

class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, **kwargs)[source]

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’} or callable, 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.

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 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_sizeint, default=30

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.

pint, default=2

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.

metricstr or callable, default=’minkowski’

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 DistanceMetric for a list of available metrics. 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.

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 Glossary for 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 parameter or 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 metrics will be same with metric_params parameter, but may also contain the p parameter value if the effective_metric_ attribute is set to ‘minkowski’.

outputs_2d_bool

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

See also

RadiusNeighborsClassifier
KNeighborsRegressor
RadiusNeighborsRegressor
NearestNeighbors

Notes

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

Warning

Regarding the Nearest Neighbors algorithms, if it is found that two neighbors, neighbor k+1 and k, have identical distances but different labels, the results will depend on the ordering of the training data.

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]]))
[[0.66666667 0.33333333]]

Methods

fit(X, y)

Fit the model using X as training data and y as target values

get_params([deep])

Get parameters for this estimator.

kneighbors([X, n_neighbors, return_distance])

Finds the K-neighbors of a point.

kneighbors_graph([X, n_neighbors, mode])

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

predict(X)

Predict the class labels for the provided data.

predict_proba(X)

Return probability estimates for the test data X.

score(X, y[, sample_weight])

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

set_params(**params)

Set the parameters of this estimator.
__init__(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None, **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, y)[source]

Fit the model using X as training data and y as target values

Parameters
X{array-like, sparse matrix, BallTree, KDTree}

Training data. If array or matrix, shape [n_samples, n_features], or [n_samples, n_samples] if metric=’precomputed’.

y{array-like, sparse matrix}

Target values of shape = [n_samples] or [n_samples, n_outputs]

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters
deepbool, default=True

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

Returns
paramsmapping of string to any

Parameter names mapped to their values.

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

Finds the K-neighbors of a point. Returns indices of and distances to the neighbors of each point.

Parameters
Xarray-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’

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

Number of neighbors to get (default is the value passed to the constructor).

return_distanceboolean, optional. Defaults to True.

If False, distances will not be returned

Returns
neigh_distarray, shape (n_queries, n_neighbors)

Array representing the lengths to points, only present if return_distance=True

neigh_indarray, shape (n_queries, n_neighbors)

Indices of the nearest points in the population matrix.

Examples

In the following example, we construct a NearestNeighbors class from an array representing our data set and ask who’s the 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 the element 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]

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

Parameters
Xarray-like, shape (n_queries, n_features), or (n_queries, n_indexed) if metric == ‘precomputed’

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

Number of neighbors for each sample. (default is value passed to the constructor).

mode{‘connectivity’, ‘distance’}, optional

Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are Euclidean distance between points.

Returns
Asparse graph in CSR format, shape = [n_queries, n_samples_fit]

n_samples_fit is the number of samples in the fitted data A[i, j] is assigned the weight of edge that connects i to j.

See also

NearestNeighbors.radius_neighbors_graph

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
Xarray-like 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
Xarray-like 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 ordered by 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 accuracy which is a harsh metric since you require for each sample that each 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) wrt. 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 pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfobject

Estimator instance.