# 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.

```# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#         Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause (C) INRIA
```

## 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()

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()
```

Total running time of the script: (0 minutes 0.224 seconds)

Related examples

Nearest Neighbors Classification

Nearest Neighbors Classification

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

SVM: Weighted samples

SVM: Weighted samples

Caching nearest neighbors

Caching nearest neighbors

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