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 knearest 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 userdefined 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 bruteforce 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 ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details. Doesn’t affectfit
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 themetric
parameter set to ‘minkowski’ andp
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 thep
parameter value if theeffective_metric_
attribute is set to ‘minkowski’. 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 fit otherwise True.
Notes
See Nearest Neighbors in the online documentation for a discussion of the choice of
algorithm
andleaf_size
.Warning
Regarding the Nearest Neighbors algorithms, if it is found that two neighbors, neighbor
k+1
andk
, have identical distances but different labels, the results will depend on the ordering of the training data.https://en.wikipedia.org/wiki/Knearest_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 knearest neighbors classifier from the training dataset.
get_params
([deep])Get parameters for this estimator.
kneighbors
([X, n_neighbors, return_distance])Finds the Kneighbors of a point.
kneighbors_graph
([X, n_neighbors, mode])Computes the (weighted) graph of kNeighbors for points in X
predict
(X)Predict the class labels for the provided data.
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.

fit
(X, y)[source]¶ Fit the knearest neighbors classifier from the training dataset.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples) if metric=’precomputed’
Training data.
 y{arraylike, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs)
Target values.
 Returns
 selfKNeighborsClassifier
The fitted knearest neighbors classifier.

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
 paramsdict
Parameter names mapped to their values.

kneighbors
(X=None, n_neighbors=None, return_distance=True)[source]¶ Finds the Kneighbors of a point.
Returns indices of and distances to the neighbors of each point.
 Parameters
 Xarraylike, 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 the value 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 if return_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 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 kNeighbors for points in X
 Parameters
 Xarraylike 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 value passed to the constructor.
 mode{‘connectivity’, ‘distance’}, default=’connectivity’
Type of returned matrix: ‘connectivity’ will return the connectivity matrix with ones and zeros, in ‘distance’ the edges are Euclidean distance between points.
 Returns
 Asparsematrix of shape (n_queries, n_samples_fit)
n_samples_fit
is the number of samples in the fitted dataA[i, j]
is assigned the weight of edge that connectsi
toj
. The matrix is of CSR format.
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
 Xarraylike 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
 Xarraylike 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 multilabel 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
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for
X
. sample_weightarraylike 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
Pipeline
). 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
 selfestimator instance
Estimator instance.