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Jul 8, 2024
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DOC Add link to plot_regression.py #29232

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087fea6
Add example link to kNN Regressor
craetona May 3, 2024
01ee0f5
Merge branch 'scikit-learn:main' into documentation
craetona Jun 10, 2024
d9b38ea
DOC improve example
adrinjalali Jun 11, 2024
6f21702
Merge branch 'main' into documentation
adrinjalali Jul 8, 2024
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21 changes: 12 additions & 9 deletions examples/neighbors/plot_regression.py
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Expand Up @@ -6,41 +6,44 @@
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

np.random.seed(0)
X = np.sort(5 * np.random.rand(40, 1), axis=0)
T = np.linspace(0, 5, 500)[:, np.newaxis]
y = np.sin(X).ravel()
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, y).predict(T)
y_ = knn.fit(X_train, y).predict(X_test)

plt.subplot(2, 1, i + 1)
plt.scatter(X, y, color="darkorange", label="data")
plt.plot(T, y_, color="navy", label="prediction")
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))
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4 changes: 4 additions & 0 deletions sklearn/neighbors/_regression.py
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Expand Up @@ -43,6 +43,10 @@ class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase):

Uniform weights are used by default.

See the following example for a demonstration of the impact of
different weighting schemes on predictions:
:ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`.

algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
Algorithm used to compute the nearest neighbors:

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