Implementation:Scikit learn Scikit learn NeighborsBase

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

Overview

Concrete tool for providing the base class and mixin infrastructure for all nearest neighbor estimators provided by scikit-learn.

Description

NeighborsBase is the abstract base class for all nearest neighbor estimators in scikit-learn. It defines common parameters such as n_neighbors, radius, algorithm, leaf_size, metric, and p. The module also provides the mixin classes KNeighborsMixin and RadiusNeighborsMixin that implement the core neighbor-querying methods, as well as utility functions like _get_weights for computing sample weights from distances.

Usage

Use NeighborsBase and its mixins when building custom nearest neighbor estimators or when understanding the internal architecture of scikit-learn's neighbors module. End users typically do not instantiate this class directly but instead use concrete subclasses such as KNeighborsClassifier or KNeighborsRegressor.

Code Reference

Source Location

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

Signature

class NeighborsBase(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta):
    def __init__(
        self,
        n_neighbors=None,
        radius=None,
        algorithm="auto",
        leaf_size=30,
        metric="minkowski",
        p=2,
        metric_params=None,
        n_jobs=None,
    ):

Import

from sklearn.neighbors._base import NeighborsBase

I/O Contract

Inputs

Name	Type	Required	Description
n_neighbors	int or None	No	Number of neighbors to use by default for kneighbors queries
radius	float or None	No	Range of parameter space for radius-based neighbors queries
algorithm	str	No	Algorithm used to compute nearest neighbors: 'auto', 'ball_tree', 'kd_tree', or 'brute'
leaf_size	int	No	Leaf size passed to BallTree or KDTree (default=30)
metric	str or callable	No	Distance metric to use (default='minkowski')
p	float	No	Power parameter for the Minkowski metric (default=2)
metric_params	dict or None	No	Additional keyword arguments for the metric function
n_jobs	int or None	No	Number of parallel jobs to run for neighbors search

Outputs

Name	Type	Description
effective_metric_	str	The metric actually used for distance computation after fitting
effective_metric_params_	dict	The 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 KNeighborsClassifier

# NeighborsBase is abstract; use a concrete subclass
clf = KNeighborsClassifier(n_neighbors=5, algorithm="auto", metric="minkowski", p=2)
clf.fit([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
print(clf.predict([[1.5, 1.5]]))

Related Pages

• Principle:Scikit_learn_Scikit_learn_Nearest_Neighbors