Implementation:Scikit learn Scikit learn KNeighborsRegressor

Knowledge Sources	Scikit_learn Scikit-learn Docs
Domains	Machine Learning, Regression
Last Updated	2026-02-08 15:00 GMT

Overview

Concrete tool for k-nearest neighbors regression provided by scikit-learn.

Description

KNeighborsRegressor predicts target values by local interpolation of the targets associated with the nearest neighbors in the training set. It supports uniform and distance-based weighting, as well as callable weight functions. The module also includes RadiusNeighborsRegressor which uses all neighbors within a fixed radius for prediction. Both regressors support multiple distance metrics and tree-based algorithms for efficient neighbor search.

Usage

Use KNeighborsRegressor when you need a non-parametric regression method that makes predictions based on the similarity of training samples. It is well-suited for problems where the relationship between features and targets is complex and locally smooth.

Code Reference

Source Location

• Repository: scikit-learn
• File: sklearn/neighbors/_regression.py

Signature

class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase):
    def __init__(
        self,
        n_neighbors=5,
        *,
        weights="uniform",
        algorithm="auto",
        leaf_size=30,
        p=2,
        metric="minkowski",
        metric_params=None,
        n_jobs=None,
    ):

Import

from sklearn.neighbors import KNeighborsRegressor

I/O Contract

Inputs

Name	Type	Required	Description
n_neighbors	int	No	Number of neighbors to use (default=5)
weights	str or callable	No	Weight function: 'uniform', 'distance', or a callable (default='uniform')
algorithm	str	No	Algorithm to compute nearest neighbors: 'auto', 'ball_tree', 'kd_tree', 'brute'
leaf_size	int	No	Leaf size for BallTree or KDTree (default=30)
p	float	No	Power parameter for Minkowski metric (default=2)
metric	str, DistanceMetric, or callable	No	Distance metric (default='minkowski')
metric_params	dict or None	No	Additional keyword arguments for the metric function
n_jobs	int or None	No	Number of parallel jobs for neighbors search

Outputs

Name	Type	Description
effective_metric_	str	The metric used for distance computation
effective_metric_params_	dict	Parameters for the effective metric
n_features_in_	int	Number of features seen during fit
n_samples_fit_	int	Number of samples in the fitted data

Usage Examples

Basic Usage

from sklearn.neighbors import KNeighborsRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split

X, y = make_regression(n_samples=200, n_features=4, noise=0.1, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

reg = KNeighborsRegressor(n_neighbors=5, weights="distance")
reg.fit(X_train, y_train)
print(f"R2 score: {reg.score(X_test, y_test):.3f}")

Related Pages

• Principle:Scikit_learn_Scikit_learn_Nearest_Neighbors