class sklearn.neighbors.KNeighborsRegressor(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2, metric=’minkowski’, metric_params=None, n_jobs=None, **kwargs)
[source]
Regression based on knearest neighbors.
The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
Read more in the User Guide.
Parameters: 


See also
NearestNeighbors
, RadiusNeighborsRegressor
, KNeighborsClassifier
, RadiusNeighborsClassifier
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/Knearest_neighbor_algorithm
>>> X = [[0], [1], [2], [3]] >>> y = [0, 0, 1, 1] >>> from sklearn.neighbors import KNeighborsRegressor >>> neigh = KNeighborsRegressor(n_neighbors=2) >>> neigh.fit(X, y) KNeighborsRegressor(...) >>> print(neigh.predict([[1.5]])) [0.5]
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 Kneighbors of a point. 
kneighbors_graph ([X, n_neighbors, mode])  Computes the (weighted) graph of kNeighbors for points in X 
predict (X)  Predict the target for the provided data 
score (X, y[, sample_weight])  Returns the coefficient of determination R^2 of the prediction. 
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]
fit(X, y)
[source]
Fit the model using X as training data and y as target values
Parameters: 


get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

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: 


Returns: 

In the following example, we construct a NeighborsClassifier 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(algorithm='auto', leaf_size=30, ...) >>> 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: 


Returns: 

>>> X = [[0], [3], [1]] >>> from sklearn.neighbors import NearestNeighbors >>> neigh = NearestNeighbors(n_neighbors=2) >>> neigh.fit(X) NearestNeighbors(algorithm='auto', leaf_size=30, ...) >>> A = neigh.kneighbors_graph(X) >>> A.toarray() array([[1., 0., 1.], [0., 1., 1.], [1., 0., 1.]])
predict(X)
[source]
Predict the target for the provided data
Parameters: 


Returns: 

score(X, y, sample_weight=None)
[source]
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1  u/v), where u is the residual sum of squares ((y_true  y_pred) ** 2).sum() and v is the total sum of squares ((y_true  y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters: 


Returns: 

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.
Returns: 


sklearn.neighbors.KNeighborsRegressor
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html