sklearn_quantile.KNeighborsQuantileRegressor

class sklearn_quantile.KNeighborsQuantileRegressor(n_neighbors=5, q=None, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)

Quantile regression based on k-nearest 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.

Added in version 0.9.

Parameters:

• n_neighbors (int, default=5) – Number of neighbors to use by default for kneighbors() queries.

• q (float or array-like, optional) – Quantiles used for prediction (values ranging from 0 to 1)

• 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.

  Uniform weights are used by default.

• 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_size (int, 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.

• p (int, 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.

• metric (str 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_params (dict, default=None) – Additional keyword arguments for the metric function.

• n_jobs (int, 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.

effective_metric_

The distance metric to use. 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.

Type:

str or callable

effective_metric_params_

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’.

Type:

dict

n_features_in_

Number of features seen during fit.

Added in version 0.24.

Type:

int

feature_names_in_

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

Added in version 1.0.

Type:

ndarray of shape (n_features_in_,)

n_samples_fit_

Number of samples in the fitted data.

Type:

int

See also

NearestNeighbors

Unsupervised learner for implementing neighbor searches.

RadiusNeighborsRegressor

Regression based on neighbors within a fixed radius.

KNeighborsClassifier

Classifier implementing the k-nearest neighbors vote.

RadiusNeighborsClassifier

Classifier implementing a vote among neighbors within a given radius.

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_neighbors_algorithm

__init__(n_neighbors=5, q=None, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)

Methods

__init__([n_neighbors, q, weights, ...])
fit(X, y)	Fit the k-nearest neighbors regressor from the training dataset.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
kneighbors([X, n_neighbors, return_distance])	Find the K-neighbors of a point.
kneighbors_graph([X, n_neighbors, mode])	Compute the (weighted) graph of k-Neighbors for points in X.
predict(X)	Predict conditional quantile of the nearest neighbours.
score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.
validate_quantiles()	Validate the quantiles inserted in the quantile regressor