Note

Go to the end to download the full example code or to run this example in your browser via JupyterLite or Binder.

Nearest Neighbors regression

Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpolation of the target using both barycenter and constant weights.

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

Generate sample data

Here we generate a few data points to use to train the model. We also generate data in the whole range of the training data to visualize how the model would react in that whole region.

import matplotlib.pyplot as plt
import numpy as np

from sklearn import neighbors

rng = np.random.RandomState(0)
X_train = np.sort(5 * rng.rand(40, 1), axis=0)
X_test = np.linspace(0, 5, 500)[:, np.newaxis]
y = np.sin(X_train).ravel()

# Add noise to targets
y[::5] += 1 * (0.5 - np.random.rand(8))

Fit regression model

Here we train a model and visualize how uniform and distance weights in prediction effect predicted values.

n_neighbors = 5

for i, weights in enumerate(["uniform", "distance"]):
    knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights)
    y_ = knn.fit(X_train, y).predict(X_test)

    plt.subplot(2, 1, i + 1)
    plt.scatter(X_train, y, color="darkorange", label="data")
    plt.plot(X_test, y_, color="navy", label="prediction")
    plt.axis("tight")
    plt.legend()
    plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights))

plt.tight_layout()
plt.show()

KNeighborsRegressor (k = 5, weights = 'uniform'), KNeighborsRegressor (k = 5, weights = 'distance')

Total running time of the script: (0 minutes 0.201 seconds)

Related examples

Nearest Neighbors Classification

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

SVM: Weighted samples

Caching nearest neighbors

© 2007–2026 The scikit-learn developers
Licensed under the 3-clause BSD License.
https://scikit-learn.org/1.9/auto_examples/neighbors/plot_regression.html