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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()